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Location: California; Sunnyvale, CA; San Francisco, CA; Seattle, WA; Redmond, Washington; Burlingame, CA; New York City Metropolitan Area; Los Angeles, CA; Washington, D.C.; Boston, MA Workplace: onsite Overview Welo Data is looking for sharp, curious, and detail-oriented individuals to join our team as Data Labeling Associate. This is not a traditional annotation role. You’ll be working directly with cutting-edge AI systems — evaluating outputs, identifying gaps, and helping improve how these systems behave in real-world scenarios. The work sits at the intersection of data quality, model evaluation, and human judgment, where your ability to think critically matters just as much as following guidelines. We’re looking for people who are naturally curious about AI, comfortable forming opinions, and confident in contributing to conversations with teammates, leads, and stakeholders. Project Details Job Title: Data Labeling Associate Hiring in: Onsite (Bay Area, Seattle, NYC, or client-dependent)  Hours: Full-time, 40 hours per week Employment Type: W2 Full-Time Employee Work Authorization: Must be authorized to work in the U.S. (no visa sponsorship) Pay Rate: $34/hour   Important: This is a 100% onsite position — remote work is not available for this role. To be considered, candidates must be located in or able to commute to one of the following cities: NYC, Seattle, Bellevue, Redmond, San Francisco, Sunnyvale, Burlingame, Austin, Los Angeles, Washington DC, Chicago, Boston, Austin. Please only apply if you meet this location requirement. What You’ll Do Evaluate AI model outputs and provide structured, high-quality feedback Perform audit-based reviews of data and model behavior — identifying patterns, edge cases, and failure modes Apply guidelines thoughtfully — and flag when they don’t reflect real-world scenarios Contribute to improving evaluation frameworks, not just executing them Identify trends in model performance and communicate insights clearly Participate in team discussions, calibrations, and stakeholder syncs Partner with leads and cross-functional teams to refine quality standards Document findings in a clear, concise, and actionable way What We’re Looking For Native-level Tamil language proficiency and a university degree (Bachelor’s or higher). B2 or superior level of English. 1–2 years of professional writing experience with strong, structured writing skills Ability to apply complex writing rules and guidelines consistently Strong understanding of safety considerations in GenAI data delivery, with 2+ years of relevant experience Strong critical thinking and attention to detail Ability to make sound judgment calls in ambiguous situations Naturally curious about AI, technology, and how systems behave Comfortable speaking up, asking questions, and contributing ideas Strong written and verbal communication skills Ability to stay consistent while working with evolving guidelines Experience in data quality, QA, annotation, or analysis is helpful — but not required Benefits Paid Vacation: 6 days Paid Company Holidays: 2 days (Memorial Day and Labor Day) Paid Sick Leave: accrued per applicable state law and company policy Medical, Dental, and Vision Insurance (eligibility applies) Health Savings Account (HSA) 401(k) Retirement Plan Employee Assistance Program Additional voluntary benefits (life, accident, critical illness, etc.) Free Gourmet Food: Free breakfast, lunch, and dinner are provided, featuring a wide variety of cuisines in multiple cafes. Micro-kitchens & Snacks: Offices are stocked with free snacks and beverages, including premium coffee and La Croix. Unique Campus Features: Some locations include roof-top nature parks Commuter Benefits: Free transport, shuttles, and sometimes bike-to-work perks. Please note that in order to verify work authorization as is required by Federal law (I-9 process), all new employees must complete a live video verification with their selected IDs and provide photos of these selected IDs within their first 3 days of employment.   To know more details (Click here)   In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire.  In addition, we employ anti-fraud checks to ensure all candidates meet the requirements of the program.     As a trusted global transformation partner, Welocalize accelerates the global business journey by enabling brands and companies to reach, engage, and grow international audiences. Welocalize delivers multilingual content transformation services in translation, localization, and adaptation for over 250 languages with a growing network of over 400,000 in-country linguistic resources. Driving innovation in language services, Welocalize delivers high-quality training data transformation solutions for NLP-enabled machine learning by blending technology and human intelligence to collect, annotate, and evaluate all content types. Our team works across locations in North America, Europe, and Asia serving our global clients in the markets that matter to them. www.welocalize.com   To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform essential functions. Why This Role This is an opportunity to move beyond traditional data work and play a direct role in how AI systems are evaluated and improved. The work is fast-moving, collaborative, and increasingly central to how modern AI systems are built and deployed.

Location: New York, NY Workplace: OnSite ABOUT US At LangChain, our mission is to enable every company to own its intelligence. We believe agents will transform how companies operate, and we're building the foundation to make that future possible. Our open-source harnesses and LangSmith platform give developers and enterprises the infrastructure to run, improve, and govern agents at scale. We've raised $460M to date, with our most recent funding round valuing the company at $3B, backed by leading investors including Sequoia, Benchmark, IVP, and CapitalG. As we continue to expand our platform and tackle some of the hardest challenges in agent engineering, every team member has the opportunity to make a meaningful impact and help shape both our technology and the future of the industry. More than 7,000 organizations rely on LangSmith, from fast-growing startups to Fortune 500 enterprises, including NVIDIA, Bridgewater, LinkedIn, Workday, and Rippling. We're just getting started on making high-performing agents easier to build and manage at scale. ABOUT THE TEAM The LangSmith team owns and builds LangChain's core platform for observability, evaluation, and production reliability of AI systems. From tracing and annotation to run rules, evaluations, and beyond, this team owns LangSmith end-to-end. If you want to define what great AI observability looks like at production scale, this is where that work gets done. ABOUT THE ROLE We're looking for a Product Manager to own key parts of LangSmith. Building AI products is changing the way software teams work; LangSmith is the observability, evals and deployment platform that makes shipping reliable AI systems possible. WHAT YOU'LL DO This role sits at the intersection of user experience and production infrastructure. You'll partner with engineers to build features that feel intuitive while solving deeply technical problems: versioning complex prompt chains, debugging multi-step agent failures, or designing evaluation systems that scale from 10 to 10,000 test cases. You'll work directly with customers — from early-stage startups shipping their first agent to enterprises running mission-critical AI systems — to understand where the platform breaks down and what capabilities unlock new workflows. - Own the roadmap for a core area of the LangSmith platform. - Work with customers to understand their challenges and define new product features. - Work with engineering to define scope, make technical tradeoffs, and ship high-impact features quickly. - Help shape product strategy alongside our founders and leadership team. WHAT YOU'LL BRING - 3+ years of product management or engineering experience, ideally building developer tools, infrastructure products, or technical SaaS platforms. - Deeply technical — you can hold your own in conversations about API design, system architecture, data pipelines, and debugging production issues. - Bias toward action — you'd rather ship, learn, and iterate than build the perfect solution upfront. - Strong written and verbal communicator who can align cross-functional teams around a shared vision. NICE TO HAVE - Computer Science, Engineering, or equivalent technical background. - Previous experience at a high-growth startup or developer-focused company. - Hands-on experience building with LLMs, agents, or AI applications. - Annual salary range: $180,000-$240,000 COMPENSATION PHILOSOPHY: We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. We are hiring for this role across a range of scope, and the compensation band reflects that. Your offer depends on what we learn about your impact, craft, leadership, and communication during the interview process. We will tell you early where you’re tracking and talk comp with you before offer stage. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations. BENEFITS Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.

Location: Remote Workplace: Remote Hi, we’re Gravie. Our mission is to create health benefits that actually benefit small and midsize businesses and their employees. Our innovative benefit solutions and services are developed and delivered by a diverse group of unique people. We encourage you to be your authentic self - we like you that way. ROLE SUMMARY Gravie is seeking a Senior Platform Engineer to build and operate the platform our engineering teams ship on. AWS should be second nature: deep enough to reach for it before documentation, and you should be as comfortable working alongside AI agents as in a terminal. The ideal candidate has genuinely operational AWS expertise, has worked in or closely with a platform team before, and knows a platform only succeeds when engineers choose it. They will own shared infrastructure, pipelines, and tooling end to end, partner with our architecture team on turning direction into paved paths teams actually adopt, and treat the engineers who use the platform as the customers they are. At Gravie, engineers work closely with Product and own outcomes end to end, and we practice agentic development to increase the scope and leverage of that ownership: engineers use AI agents to help develop specs and plans, then to execute substantial multi-step engineering work, while setting guardrails and acceptance criteria and owning the quality of everything that ships. As agents take on more of the execution, the bar on problem framing, architectural judgment, system thinking, and risk identification gets higher, not lower. HOW OUR PLATFORM TEAM WORKS If you have worked on a platform team, this will be familiar. If you have not, read it carefully, because it is the job. - The platform is a product and engineers are its users. Adoption is the measure of success, not tickets closed. If teams route around something we built, that is our problem to solve, not their failure to comply. - Self-service over gatekeeping. If the answer to a common request is a ticket to us, that is a bug in the platform. - We pave roads, we do not police them. Standards belong in pipelines, templates, and defaults, not in reminders and review comments. - Small team, wide surface. Leverage matters more than heroics, which is why AI tooling and automation are not side projects here - they are how the surface stays covered. - We operate what we ship. On-call for platform services is ours. So is the postmortem, and so is fixing the class of problem rather than the instance. - Documentation is a deliverable. A capability nobody can find or adopt without asking us is not finished. KEY RESPONSIBILITIES - Build and operate the shared platform: CI/CD pipelines, service templates, deployment tooling, and the shared infrastructure services teams depend on. - Own AWS infrastructure as code across multiple accounts, in Terraform, CDK, or both, held to the same standard as application code, including review, testing, and a real rollback story. - Implement and sustain the paved paths teams build on, partnering with architecture on direction while owning the operational reality once a path is live. - Operate what you build: on-call for platform services, incident response, the postmortem, and fixing the class of problem rather than the instance. - Build observability in by default: metrics, logs, traces, SLOs, and alerts that page a human only when a human is actually needed. - Own platform security posture alongside Security: identity and permission boundaries, secrets handling, network and data isolation, and remediation of findings in a HIPAA and SOC 2 environment. - Make cloud spend attributable and defensible through tagging discipline, cost visibility, and removing waste before it compounds. - Use AI agents as a normal part of daily workflow, and build them into the platform where they create leverage, including code review, alert triage, migrations, and repetitive remediation. - Automate toil out of the platform, reducing the number of requests that require a person at all, and measure whether it worked. - Treat documentation and developer onboarding as part of the deliverable, so teams can adopt platform capability without asking first. - Drive adoption of platform capability and standards, including the unglamorous migration work needed to move existing services. - Raise the engineering bar through code review and mentorship, and help other engineers get more out of AI tooling. QUALIFICATIONS - Five or more years of software or infrastructure engineering experience, including ownership of production systems that you also operated. - AWS to a depth that is genuinely second nature - reasoning about IAM policy evaluation, VPC routing and DNS, and container or serverless runtime behavior without reaching for documentation first, and having been the person who diagnosed it under real pressure. - Hands-on ownership of a multi-account AWS environment, including identity boundaries, networking, managed data services, and cost and tagging governance. - Infrastructure as code as your default rather than your fallback, in Terraform, CDK, or both, including how you review it, test it, and reason about blast radius before applying it. - Strong CI/CD engineering, including shared or templated pipelines consumed by more than one team. - Experience working in or closely with a platform, infrastructure, or developer experience team, and a clear point of view on what makes a platform get adopted rather than merely tolerated. - Real comfort with AI tooling in daily engineering work - using coding agents such as Claude Code to do meaningful multi-step work, able to describe where they fail and how you catch it, and keeping ownership of what ships. - Production operations experience: on-call, incident response, postmortems, and SLO thinking. - Strong programming ability in at least one language such as Python, Go, TypeScript, or a JVM language, enough to build tooling rather than only configure it. - Sound judgment on security, secrets, access control, and data handling, ideally in a regulated or audited environment. - Clear written communication - on a platform team, documentation and a well-argued change proposal are load-bearing, not overhead. PREFERRED QUALIFICATIONS - Healthcare or another regulated domain, including HIPAA and SOC 2 obligations. - Previous experience in a venture-backed high growth company. - GitLab CI at scale, including templated pipelines with typed inputs consumed across many repositories. - Datadog in depth: monitors, dashboards, SLOs, and keeping observability cost under control. - Container orchestration at production scale, such as ECS or Kubernetes. - Building AI tooling rather than only using it, such as MCP servers, Claude Code plugins or skills, or agent harnesses. - Dependency and currency automation, such as Renovate, and the lifecycle policy around it. - Delivery metrics work, including DORA, and the definitional arguments that come with it. - Familiarity with Team Topologies vocabulary and where platform teams fit alongside stream-aligned and enabling teams. - Experience decommissioning a platform capability, not only launching one. A LITTLE MORE ABOUT US: - We know healthcare. Our company was founded and is still led by industry veterans who have started and grown several market-leading companies in the space. - We have raised money from top tier investors who share the same long-term vision as we do of building an industry defining company that will endure over the long run. We are well capitalized. - Our clients love us. Customer satisfaction rates among employees using Gravie health plans consistently rank above 80% – nearly 40 points above the industry average. - Our culture is unique. We tend to be non-hierarchical, merit-driven, opinionated but kind people who thrive working in a high-performance, fast-paced environment. People at Gravie care deeply about making a positive impact in the lives of the people we serve. BENEFITS Our unique benefits program is the gravy, i.e., the special sauce that sets our compensation package apart. In addition to standard health and wellness benefits, Gravie’s package includes alternative medicine coverage, flexible PTO, up to 16 weeks paid parental leave, paid holidays, a 401k program, transportation perks, education reimbursement, and 2 days of paid paw-ternity leave. JOB APPLICANTS If you apply for employment with Gravie, personal information collected via our applicant tracking vendor is subject to our standalone California Job Applicant Notice at Collection, accessible directly within the application workflow and separate from this Policy.

Location: Remote Workplace: Remote Hi, we’re Gravie. Our mission is to create health benefits that actually benefit small and midsize businesses and their employees. Our innovative benefit solutions and services are developed and delivered by a diverse group of unique people. We encourage you to be your authentic self - we like you that way. ROLE SUMMARY Gravie is seeking a hands-on Staff Platform Engineer to do three things at once: build the golden roads and paved paths our engineering teams build on, define the north star architecture the organization moves toward, and design and deliver our AI-enabled capabilities. This is the hub role for technical direction at Gravie: long-term architectural direction and the standards that carry it converge here and radiate outward across teams. The ideal candidate combines deep distributed systems and AWS experience with real production generative AI experience, and is equally credible building a paved path teams voluntarily adopt and setting a target-state architecture the whole organization will inherit. They have established standards, reference implementations, and paved roads that engineers actually use, not just architecture documents. They will partner across Engineering, Product, Data, Security, and Infrastructure, and will work most closely with our Platform Engineering team, which is the primary vehicle for making change stick across the organization. At Gravie, engineers work closely with Product and own outcomes end to end, and we practice agentic development to increase the scope and leverage of that ownership: engineers use AI agents to help develop specs and plans, then to execute substantial multi-step engineering work, while setting guardrails and acceptance criteria and owning the quality of everything that ships. As agents take on more of the execution, the bar on problem framing, architectural judgment, system thinking, and risk identification gets higher, not lower. This is a deliberately split role: roughly 40% golden roads and paved paths (hands-on, reference implementations, shared libraries, service templates, scaffolding, pipelines - a path isn't paved until a team other than yours has shipped on it), 30% north star architecture (published decisions that get adopted and enforced by tooling, not just written down), and 30% AI solutions (agent systems, retrieval and context design, evaluation, and guardrails). The mix shifts by quarter. Being the hub does not mean being the bottleneck - a good year looks like teams making more decisions on their own because direction and the paved path are clear enough to follow without asking. The role is planned and reviewed on this split, not funded out of leftover capacity; part of the job is saying out loud when the split is breaking toward a support desk. KEY RESPONSIBILITIES - Build the paved paths teams build on, including reference implementations, shared libraries, service templates, scaffolding, and pipeline templates, so the recommended way is also the easiest way. - Treat the paved road as a product with real users: measure adoption and time to first success, and fix the parts people route around. - Do the first real implementation yourself, on a real workload with a real team, not in a demo repository. - Build the distributed systems patterns the paths encode: backend services, event-driven workers, job pipelines, APIs, and relational data models. - Partner with the Platform Engineering team as the primary delivery vehicle - build paved paths with them, plan into their roadmap, and hand off sustaining ownership of what becomes shared infrastructure. - Own migration and adoption, including moving existing services onto the path and retiring what it replaces. - Stay in code review across teams and close to production incidents as primary evidence of where the path is failing. - Define and publish the target-state architecture, with clear owners and decision dates, and keep it current as the business changes. - Own the AWS foundations product and AI workloads depend on: multi-account structure and identity boundaries, network and data isolation, compute and serverless runtimes, managed data services, and tagging and cost governance. - Drive standards adoption to completion - from documents into pipelines, with adoption measured across repositories and teams. - Lead buy, build, and reuse decisions, evaluating models, frameworks, and vendors on accuracy, latency, reliability, privacy, security, and cost evidence. - Reduce surface area as deliberately as you add it - consolidate overlapping tools and standards and own the sunset path for what gets replaced. - Design and build production-grade AI agent systems, including multi-agent workflows, orchestration layers, and supporting services. - Design retrieval, context, and memory systems that ground AI outputs, including how sensitive data is scoped, redacted, and audited. - Develop AI-powered decision-support systems meeting the accuracy, traceability, and explainability requirements of healthcare and other regulated environments. - Build product-quality user experiences using React and TypeScript where AI capability has to become understandable and actionable. - Establish patterns for AI quality assurance - automated evaluations, regression testing, groundedness checks, and compliance guardrails - so those requirements are met by default. - Establish one standard way to build, run, evaluate, and operate AI systems. - Act as the connective point across teams: surface duplicated effort, pick up decisions with no owner, and align teams heading toward the same problem. - Drive cross-team communication - writing, demos, and working sessions - so direction and tradeoffs are understood by teams not in the room. - Build alignment where incentives differ across teams, and surface disagreement rather than resolving it quietly. - Contribute to roadmap and capacity planning so paved path, foundational, and AI work is sequenced alongside product commitments. - Work through the Platform Engineering team to effect change at organization scale rather than standing up competing surfaces. - Facilitate collaboration between engineering teams, Product, Data, Security, and Infrastructure, including running forums where cross-cutting work gets planned and unblocked. - Represent technical direction to leadership and carry business context back to engineering. - Partner with Product to shape the roadmap and translate ambiguous business problems into architecture. - Raise the engineering bar through design review, mentorship, and a working decision-record practice, and make architectural context available to both engineers and agents. - Partner with Security, Compliance, and Data on AI governance, access control, and data handling, designed in rather than retrofitted. QUALIFICATIONS - Eight or more years of software engineering experience, including time with architecture scope and an organization-wide remit, and ownership of complex systems from design through production. - Still shipping: writes and reviews code today, can open a meaningful merge request in an unfamiliar codebase within the first weeks. - Experience building internal platform capability that other teams voluntarily adopted - can name the paved path or golden road built, how many teams moved onto it, and what they routed around. - Hands-on experience building and operating production AI applications, not only prototypes, including evaluation, monitoring, failure handling, guardrails, latency, and cost control. - Deep AWS expertise across a multi-account organization: identity and permission boundaries, networking, compute and serverless runtimes, managed data services, managed model services, and cost and tagging governance. Infrastructure as code in Terraform, CDK, or both. - A track record of establishing architectural foundations where little existed - specific standards introduced, how they were adopted by teams that did not report to the candidate, and which failed and why. - Hands-on experience with agentic software development - using AI coding agents to create specs and plans, execute meaningful multi-step work, run and fix tests, validate results, and iterate while owning architecture, quality, and what ships. - Deep expertise in at least one primary area (Python or JVM backend and distributed systems, or React and TypeScript product engineering), plus ability to work across the full system. - Experience with retrieval, embeddings, context, memory, evaluation, monitoring, or AI agent architectures and workflows. - Strong judgment in testing, monitoring, failure handling, privacy, security, access control, and data governance, carried into a regulated or audited environment. - Experience working with or inside a platform or infrastructure team as the delivery vehicle for organization-wide change, including how ownership was divided and how a parallel platform was avoided. - Experience serving as a central technical point across multiple teams, with evidence of making those teams faster rather than becoming their approval gate. - Demonstrated ability to create alignment across disagreeing teams, and to contribute to roadmap and capacity planning so foundational work is actually scheduled. - Clear written communication and effective collaboration across technical and non-technical teams; influence without authority is the primary tool of this role. PREFERRED QUALIFICATIONS - Healthcare, health insurance, or benefits administration, including ICHRA, claims, eligibility and enrollment, or interoperability standards such as EDI and FHIR. - Previous experience in a venture-backed high growth company. - Internal developer platform or developer experience work, including golden path or paved road programs and adoption measurement. - Experience with GitLab CI at scale, including shared and templated pipelines. - Production observability and LLM observability practice, including Datadog. - Event-driven and streaming architecture, such as Kafka or Flink. - Practical experience with agent tooling and standards such as Claude Code, the Model Context Protocol, and agent SDKs. - Experience defining an AI governance or technology radar process alongside a security or compliance partner. A LITTLE MORE ABOUT US: - We know healthcare. Our company was founded and is still led by industry veterans who have started and grown several market-leading companies in the space. - We have raised money from top tier investors who share the same long-term vision as we do of building an industry defining company that will endure over the long run. We are well capitalized. - Our clients love us. Customer satisfaction rates among employees using Gravie health plans consistently rank above 80% – nearly 40 points above the industry average. - Our culture is unique. We tend to be non-hierarchical, merit-driven, opinionated but kind people who thrive working in a high-performance, fast-paced environment. People at Gravie care deeply about making a positive impact in the lives of the people we serve. BENEFITS Our unique benefits program is the gravy, i.e., the special sauce that sets our compensation package apart. In addition to standard health and wellness benefits, Gravie’s package includes alternative medicine coverage, flexible PTO, up to 16 weeks paid parental leave, paid holidays, a 401k program, transportation perks, education reimbursement, and 2 days of paid paw-ternity leave. JOB APPLICANTS If you apply for employment with Gravie, personal information collected via our applicant tracking vendor is subject to our standalone California Job Applicant Notice at Collection, accessible directly within the application workflow and separate from this Policy.

Location: California; Sunnyvale, CA; San Francisco, CA; Seattle, WA; Redmond, Washington; Burlingame, CA; New York City Metropolitan Area; Los Angeles, CA; Washington, D.C.; Boston, MA Workplace: onsite Overview Welo Data is looking for sharp, curious, and detail-oriented individuals to join our team as Data Labeling Associate. This is not a traditional annotation role. You’ll be working directly with cutting-edge AI systems — evaluating outputs, identifying gaps, and helping improve how these systems behave in real-world scenarios. The work sits at the intersection of data quality, model evaluation, and human judgment, where your ability to think critically matters just as much as following guidelines. We’re looking for people who are naturally curious about AI, comfortable forming opinions, and confident in contributing to conversations with teammates, leads, and stakeholders. Project Details Job Title: Data Labeling Associate Hiring in: Onsite (Bay Area, Seattle, NYC, or client-dependent)  Hours: Full-time, 40 hours per week Employment Type: W2 Full-Time Employee Work Authorization: Must be authorized to work in the U.S. (no visa sponsorship) Pay Rate: $34/hour   Important: This is a 100% onsite position — remote work is not available for this role. To be considered, candidates must be located in or able to commute to one of the following cities: NYC, Seattle, Bellevue, Redmond, San Francisco, Sunnyvale, Burlingame, Los Angeles, Washington DC, Boston. Please only apply if you meet this location requirement. What You’ll Do Evaluate AI model outputs and provide structured, high-quality feedback Perform audit-based reviews of data and model behavior — identifying patterns, edge cases, and failure modes Apply guidelines thoughtfully — and flag when they don’t reflect real-world scenarios Contribute to improving evaluation frameworks, not just executing them Identify trends in model performance and communicate insights clearly Participate in team discussions, calibrations, and stakeholder syncs Partner with leads and cross-functional teams to refine quality standards Document findings in a clear, concise, and actionable way What We’re Looking For Native-level Marathi language proficiency and a university degree (Bachelor’s or higher). B2 or superior level of English. 1–2 years of professional writing experience with strong, structured writing skills Ability to apply complex writing rules and guidelines consistently Strong understanding of safety considerations in GenAI data delivery, with 2+ years of relevant experience Strong critical thinking and attention to detail Ability to make sound judgment calls in ambiguous situations Naturally curious about AI, technology, and how systems behave Comfortable speaking up, asking questions, and contributing ideas Strong written and verbal communication skills Ability to stay consistent while working with evolving guidelines Experience in data quality, QA, annotation, or analysis is helpful — but not required Benefits Paid Vacation: 6 days Paid Company Holidays: 2 days (Memorial Day and Labor Day) Paid Sick Leave: accrued per applicable state law and company policy Medical, Dental, and Vision Insurance (eligibility applies) Health Savings Account (HSA) 401(k) Retirement Plan Employee Assistance Program Additional voluntary benefits (life, accident, critical illness, etc.) Free Gourmet Food: Free breakfast, lunch, and dinner are provided, featuring a wide variety of cuisines in multiple cafes. Micro-kitchens & Snacks: Offices are stocked with free snacks and beverages, including premium coffee and La Croix. Unique Campus Features: Some locations include roof-top nature parks Commuter Benefits: Free transport, shuttles, and sometimes bike-to-work perks. Please note that in order to verify work authorization as is required by Federal law (I-9 process), all new employees must complete a live video verification with their selected IDs and provide photos of these selected IDs within their first 3 days of employment.   To know more details (Click here)   In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire.  In addition, we employ anti-fraud checks to ensure all candidates meet the requirements of the program.     As a trusted global transformation partner, Welocalize accelerates the global business journey by enabling brands and companies to reach, engage, and grow international audiences. Welocalize delivers multilingual content transformation services in translation, localization, and adaptation for over 250 languages with a growing network of over 400,000 in-country linguistic resources. Driving innovation in language services, Welocalize delivers high-quality training data transformation solutions for NLP-enabled machine learning by blending technology and human intelligence to collect, annotate, and evaluate all content types. Our team works across locations in North America, Europe, and Asia serving our global clients in the markets that matter to them. www.welocalize.com   To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform essential functions. Why This Role This is an opportunity to move beyond traditional data work and play a direct role in how AI systems are evaluated and improved. The work is fast-moving, collaborative, and increasingly central to how modern AI systems are built and deployed.

Location: Home Based - Americas; Home based - EMEA Canonical is a leading provider of open source software and operating systems to the global enterprise and technology markets. Our platform, Ubuntu, is widely used in breakthrough enterprise initiatives such as public cloud, data science, AI, engineering innovation, and IoT. Our customers include the world's leading public cloud and silicon providers, as well as industry leaders across many sectors. Canonical is a pioneer of global distributed collaboration, with 1,200+ colleagues in 75+ countries and very few office-based roles. Teams meet two to four times yearly in person, in interesting locations around the world, to align on strategy and execution. The company is founder-led, profitable, and growing. We are hiring an Engineering Manager for Distributed Systems Testing to lead a team responsible for the quality engineering platform that enables deployment and validation of distributed systems across Canonical's cloud portfolio. We see quality engineering as a first-class engineering practice. Our Distributed Systems Testing team ensures the performance, correctness, resilience and interoperability of Canonical cloud solutions built with technologies such as Juju, Terraform, OpenStack and Kubernetes. These systems are deployed and tested across diverse environments, from bare-metal to the public cloud. As Engineering Manager, you will combine strong software engineering judgement with people leadership. You will develop engineering talent, establish effective engineering practices, shape technical direction with senior engineers, and ensure the team delivers high-quality testing infrastructure and automation that enables engineering teams across Canonical to ship software with confidence. We believe engineering managers should be outstanding engineers themselves. You should be comfortable reviewing patches, evaluating software design, debugging a distributed system, or discussing test architecture. You will contribute technically where appropriate to set a high standard, while recognizing that your greatest impact comes from shaping the direction, focus, effectiveness, and growth of the whole team. You will work closely with senior engineers and engineering leaders across Canonical to define priorities, improve testing capabilities, and advance how we validate increasingly complex cloud-native and distributed systems. Location: This is a globally remote role, typically leading a team concentrated within a major time zone such as EMEA or the Americas. What you'll do Lead, coach, and develop a high-performing team of software and quality engineers, ranging from graduate to senior level Set clear expectations, strengthen engineering practices, and ensure timely delivery against agreed goals Partner with senior engineers to shape technical strategy, architecture, roadmap, and engineering standards Guide the development of automated testing infrastructure for the reliability, interoperability, and resilience of distributed systems Improve CI pipelines, testing tools, and reporting used to validate Canonical's products and solutions across bare-metal, private and public cloud environments Enable engineering teams across Canonical to adopt effective distributed-systems testing practices and tooling Contribute technical leadership through design and code reviews, debugging, and hands-on engineering where appropriate Represent the team with stakeholders and collaborate across Canonical on quality engineering, infrastructure, and developer experience What we're looking for in you An exceptional academic track record from both high school and university Experience in leading, coaching, and mentoring software engineers Professional software engineering experience with Python or Go Expertise in distributed systems, cloud infrastructure, quality engineering, or a closely related domain Strong knowledge of modern software testing processes, strategies, and automation Experience with Linux (Debian or Ubuntu preferred) Strong technical judgement and the ability to engage meaningfully in code reviews, architecture discussions, and software design Experience working in an agile development environment with the ability to organize a team to deliver high-quality results Professional written and spoken English with excellent communication and presentation skills Great interpersonal skills, curiosity, flexibility, and accountability, demonstrating passion, thoughtfulness, and self-motivation Ability to travel twice a year, for company events up to two weeks each Additional skills we value Experience with any of the following: Cloud technologies such as Kubernetes, OpenStack, Juju, Terraform, AWS, Google Cloud, or Azure Designing testing strategies for distributed or cloud-native systems Building or operating large-scale CI/CD and test infrastructure Applying AI or machine learning to test analysis, failure classification, or engineering workflows Deploying, operating, or debugging distributed systems Leading globally distributed teams What we offer colleagues We consider geographical location, experience, and performance in shaping compensation worldwide. We revisit compensation annually, and more often for graduates and associates, to ensure we recognize outstanding performance. In addition to base pay, we offer a performance-driven annual bonus. We provide all team members with additional benefits that reflect our values and ideals. We balance our programmes to meet local needs and ensure fairness globally. Distributed work environment with twice-yearly team sprints in person Personal learning and development budget of USD 2,000 per year Annual compensation review Recognition rewards Annual holiday leave Maternity and paternity leave Employee Assistance Programme and wellness support Opportunity to travel to new locations to meet colleagues Priority Pass and travel upgrades for long-haul company events About Canonical Canonical is a pioneering tech firm at the forefront of the global move to open source. As the company that publishes Ubuntu, one of the most important open source projects and a leading platform for AI, IoT, and the cloud, we are changing the world of software. We recruit globally and set a very high standard for people joining the company. We expect excellence; to succeed, we need to be the best at what we do. Most colleagues at Canonical have worked from home since our inception in 2004. Working here is a step into the future and will challenge you to think differently, work smarter, learn new skills, and raise your game. Canonical is an equal opportunity employer We are proud to foster a workplace free from discrimination. Diversity of experience, perspectives, and background create a better work environment and better products. Whatever your identity, we will give your application fair consideration. Requisition ID: 1361

Location: Hong Kong, Hong Kong SAR; Singapore, Singapore Who We Are At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom. OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). OKX is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves. Across our multiple offices globally, we are united by our core principles: We Before Me , Do the Right Thing , and Get Things Done . These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er. OKX is part of OKG, a group that brings the value of Blockchain to users around the world, through our leading products OKX, OKX Wallet, OKLink and more. About the Opportunity We are building a scalable financial operations platform to support the accurate movement, recording, reconciliation and reporting of fiat and digital assets. The Head of Financial Operations Platform will define and deliver the architecture that connects our trading, payment, banking, custody, blockchain, wallet and finance systems. This is a senior, hands-on platform leadership role. The successful candidate will establish trusted books and records, automate settlement and reconciliation, create effective exception workflows and give Operations and Finance the tools required to maintain accurate client and corporate asset records on a daily and, where feasible, near-real-time basis. The role owns platform capability and delivery. Client Asset Operations and Finance retain responsibility for executing, reviewing and signing off the underlying operational and accounting controls. What You Will Own Product strategy and multi-year roadmap for the financial operations platform. The canonical transaction and financial-event model across fiat and digital assets. Client and corporate asset subledgers, balances, positions, journals and transaction histories. Settlement and asset-movement workflows across banks, payment providers, custodians, wallets, blockchains and trading venues. Multi-way transaction- and balance-level reconciliation across operational sources, internal records and the finance system. Exception management, operational dashboards, control evidence, audit trails and management reporting. Build-versus-buy assessments, strategic vendors, migration, backfilling, parallel runs and legacy-process retirement. First Year Outcomes Document the end-to-end transaction, asset and accounting architecture. Establish an approved platform roadmap and target operating model. Define the canonical financial-event, subledger and reconciliation data model. Deliver systematic reconciliation for the highest-risk fiat and digital asset flows. Introduce centralized exception ownership, ageing, escalation and evidence. Reduce spreadsheet dependency and engineering-dependent investigations. Improve alignment between operational records, customer liabilities, external asset records and the finance system. Deliver a credible path from daily controls to near-real-time monitoring for material flows. What You’ll Be Doing Product Strategy and Architecture - Establish the target vision, operating principles and roadmap for ledger, settlement, reconciliation and client asset controls. - Map end-to-end funds and asset flows across deposits, withdrawals, trades, fees, transfers, financing, staking and other relevant products. - Partner with Architecture and Engineering to define scalable services, APIs, event schemas, data contracts, system boundaries and resilience requirements. - Make evidence-based build-versus-buy decisions and prevent fragmented point solutions from becoming the operating model. Ledger and Books and Records - Define requirements for double-entry ledgering, journals, balances, positions and transaction traceability. - Establish clear lineage from the originating operational event through the subledger and financial reporting environment. - Ensure the platform handles reversals, corrections, fees, foreign exchange, internal transfers, failed transactions, duplicated events and delayed events. - Work with Finance to maintain controlled alignment between operational books and records and the ERP or general ledger. Settlement and Reconciliation - Design workflows for fiat and digital asset settlement, including cut-offs, confirmations, status management, retries and failure handling. - Build automated multi-way reconciliation across trading engines, payment systems, banks, custodians, wallets, blockchain records, subledgers and ERP systems. - Define configurable matching rules, tolerances, materiality levels, frequencies and escalation thresholds. - Create near-real-time detection for missing, duplicated, delayed or incorrectly recorded transactions where feasible and proportionate. Operational Tooling and Controls - Build operator interfaces that support investigation and resolution without routine dependence on engineering queries or spreadsheets. - Incorporate maker-checker approvals, role-based access, segregation of duties, evidence retention and complete audit histories. - Define monitoring and alerting for data completeness, balance integrity, settlement failures, reconciliation breaks and control execution. - Support audit, regulatory examination and financial-reporting requirements with reproducible system evidence. Delivery and Stakeholder Leadership - Lead discovery, requirements definition, prioritization, testing, rollout and post-launch improvement. - Write clear product requirements, workflow diagrams, data definitions, control requirements and acceptance criteria. - Coordinate operational readiness across Product, Engineering, Operations, Finance, Treasury, Custody, Risk and Compliance. - Lead data migration, backfilling and parallel-run validation before replacing existing controls or processes. - Communicate roadmap priorities, risks, dependencies and measurable outcomes to senior management. What We Look For In You Eight or more years in fintech, financial services, digital assets, brokerage, custody, payments or capital-markets technology. Five or more years of product-management, platform leadership, technical program leadership or comparable engineering experience. Demonstrated delivery of a ledger, reconciliation platform, settlement system, payments infrastructure, brokerage books-and-records platform or digital asset back office. Strong understanding of transaction lifecycles, balances, positions, journal entries, reconciliation and financial controls. Technical fluency with APIs, event-driven systems, data schemas, batch and streaming pipelines, integrations, idempotency and failure handling. Ability to work directly with transaction-level data; practical SQL proficiency is strongly preferred. Track record of delivering mission-critical systems in regulated or control-intensive environments. Strong written communication, structured problem-solving and senior stakeholder-management skills. Nice to Haves Experience with both fiat and digital asset transaction flows. Knowledge of blockchain transactions, wallets, custodians and on-chain settlement. Experience integrating with ERP systems such as Oracle, SAP, NetSuite or Microsoft Dynamics. Familiarity with client asset segregation, safeguarding, books and records or comparable regulatory requirements. Experience with high-volume, continuously operating financial platforms. Knowledge of double-entry accounting and financial-close processes. Experience replacing manual reconciliation with automated platform capabilities. Experience assessing or implementing platforms such as Crypto or comparable ledger, reconciliation and digital asset accounting solutions. Perks & Benefits Competitive total compensation package L&D programs and Education subsidy for employees' growth and development Various team building programs and company events Wellness and meal allowances Comprehensive healthcare schemes for employees and dependants More that we love to tell you along the process! Notice: All official OKX vacancies are published on this website. While roles may appear on selected third-party platforms from time to time, information on other sites may be inaccurate or outdated. If in doubt, please apply directly through our official careers website. Information collected and processed as part of the recruitment process of any job application you choose to submit is subject to OKX 's Candidate Privacy Notice .

Location: Toronto, Ontario, Canada Workplace: Hybrid ABOUT SENTRY Software runs the world and the pace is faster than ever. Sentry helps developers fix errors and performance issues before users notice, so teams can spend less time firefighting and more time building. Trusted by 200,000+ organizations, Sentry is today’s application monitoring standard and our team is building its AI-native future. ABOUT THE ROLE At Sentry, Support is an engineering discipline. Our customers are the greatest technical minds in the world—developers at elite enterprises building the future of software—and they deserve answers that go deeper than a knowledge base link. We are architecting the Technical Support engine. We’re looking for a veteran engineer to help us redefine the standard of technical support by combining deep human expertise with autonomous agentic systems. You are a debugger of both code and systems. You will treat support volume as a data signal to build automated resolution paths, ensuring our human engineers only touch the most complex, high-impact architectural puzzles. Sentry Support Engineers aren't just clearing queues; they are Orchestrators. You will engage with our users across GitHub, Discord, and our internal systems, while acting as the Technical Lead for our Agentic Ops. You ensure that when a developer asks a complex question, our systems have the right context and a seamless "Human-in-the-Loop" path to you when deep, nuanced expertise is required. IN THIS ROLE YOU WILL Master the Sentry Ecosystem & Support Elite Developers - Deep-Dive Debugging: Perform root-cause analysis on complex issues and distributed tracing gaps across polyglot environments. - Support the Great Minds: Act as a strategic consultant for senior engineers at our largest enterprise customers, solving high-stakes architectural challenges that push the boundaries of observability. - Troubleshoot SDK Implementations: Go deep into the source code of Sentry’s SDKs to help developers instrument complex frameworks and custom environments. - Engineering Feedback Loop: Serve as the bridge between the world's best developers and our product teams, using data from your systems to prioritize high-impact platform fixes. Engineer Agentic Operations (The Scaling Engine) - Deterministic Support Paths: Engineer the logic that powers our support agents, moving beyond probabilistic guesses to high-fidelity, autonomous resolutions. - Knowledge Engineering: Build the right context into our systems so they can resolve issues consistently and indefinitely. - Agentic Tooling Development: Use MCP (Model Context Protocol) and Sentry’s internal APIs to build "skills" for our agents, allowing them to perform complex lookups or state-checks autonomously. - The Seer Philosophy: We’ve already proven AI can handle autonomous production fixes with Seer. You will apply that same philosophy to the support lifecycle—moving us from a "Help Desk" to an "Autonomous Resolution Engine." YOU’LL LOVE THIS JOB IF - You love eliminating Non-scalable effort: you enjoy automating repetitive work and take pride in building systems that save time and effort. - You’re a Full-Stack Troubleshooter: You enjoy jumping from a React frontend issue to a Python backend trace to a CI/CD pipeline failure in a single afternoon - You Value Evidence-Based Support: You prefer looking at data trends over anecdotal evidence to decide where to build automation next. - You’re a Developer Advocate: You want to guide the industry on how elite engineers and AI co-exist in a high-density technical ecosystem. QUALIFICATIONS - 5–8+ years of experience in technical support engineering, systems engineering, or software development, with a track record of solving high-complexity SaaS problems. - Strong Coding Foundations: Mastery of Python, JavaScript, Java, or Ruby. You are comfortable reading SDK source code and identifying bugs in production-level repositories. - Systems Depth: Deep understanding of distributed systems, microservices, APIs, and the "path of a pixel" from frontend to database. - AI & Automation Literacy: Practical familiarity with LLM orchestration (RAG, context windows, system prompts) and a critical eye for where AI succeeds versus where it requires human intervention. - Exceptional Communication: The ability to explain complex architectural concepts to a human and write precise "system instructions" for an AI. BONUS QUALIFICATIONS - Open Source Contributor: An active presence on GitHub or experience maintaining open-source libraries. - Former Software Developer: You’ve spent time in the trenches as a full-time dev and know exactly what keeps our customers up at night. The base salary range (or hourly wage range, if applicable) that Sentry reasonably expects to pay for this position is $125,000 to $150,000. A successful candidate’s actual base salary (or hourly wage) amount will be determined by a variety of relevant factors including, without limitation, the candidate’s work location, education, work and other relevant experience, skills, and job-related knowledge. A successful candidate will be eligible to participate in Sentry’s employee benefit plans/programs applicable to the candidate’s position (including incentive compensation, equity grants, paid time off, and group health insurance coverage). See Sentry Benefits https://sentry.io/careers/ for more details about the Company’s benefit plans/programs. EQUAL OPPORTUNITY AT SENTRY Sentry is committed to providing equal employment opportunities to its employees and candidates for employment regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, or other legally-protected characteristic. This commitment includes the provision of reasonable accommodations to employees and candidates for employment with physical or mental disabilities who require such accommodations in order to (a) perform the essential functions of their jobs, or (b) seek employment with Sentry. We strive to build a diverse team, with an inclusive culture where every teammate can thrive. Sentry is an open-source company because we believe that everyone, everywhere, should have the ability and tools to make great software. Software should be accessible. That starts with making our industry accessible. If you need assistance or an accommodation due to a disability, you may contact us at accommodations@sentry.io. Want to learn more about how Sentry handles applicant data? Get the details in our Applicant Privacy Policy https://sentry.io/careers/applicantprivacy/.

Location: Hybrid About Us At Cloudflare, we are on a mission to help build a better Internet. Today the company runs one of the world’s largest networks that powers millions of websites and other Internet properties for customers ranging from individual bloggers to SMBs to Fortune 500 companies. Cloudflare protects and accelerates any Internet application online without adding hardware, installing software, or changing a line of code. Internet properties powered by Cloudflare all have web traffic routed through its intelligent global network, which gets smarter with every request. As a result, they see significant improvement in performance and a decrease in spam and other attacks. Cloudflare was named to Entrepreneur Magazine’s Top Company Cultures list and ranked among the World’s Most Innovative Companies by Fast Company. At Cloudflare, we’re not looking for people who wait for a polished roadmap; we’re looking for the builders who see the cracks in the Internet that everyone else has simply learned to live with. We value candidates who have the instinct to spot a "normalized" problem and the AI-native curiosity to create a solution using the latest tools. Our culture is built on iteration, leveraging AI to ship faster today to make it better tomorrow, while ensuring that every improvement, no matter how small, is shared across the team to lift everyone up. If you’re the type of person who values curiosity over bureaucracy, and that AI is a partner in solving tough problems to keep the Internet moving forward, you’ll fit right in. Available Locations: Austin, TX, New York, NY, Seattle, WA About the Role Cloudflare One brings together secure access, network connectivity, and data protection. Our products generate massive amounts of signals about how people and agents access applications, use the network, and handle data—but a security practitioner needs more than isolated events. They need to understand what happened, whether it is unusual, why it matters, and what to investigate next. The UEBA (User and Entity Behavior Analytics) team builds the systems behind those answers. We ingest and evaluate Cloudflare One signals, produce high fidelity user-risk events, maintain risk scores, and make that information available to customer-facing experiences and other Cloudflare services. Today, we are focused on expanding our behavioral detections and improving the clarity of the resulting investigations and integrations. As a Systems Engineer, you will build and operate production services in this pipeline. You might implement a detection for an unusual pattern of DNS activity or data movement, improve how events contribute to a user’s risk score, or make risk-event history easier for customers to investigate. You will own well-defined projects through design, implementation, testing, rollout, and operational follow-through, working with other engineers, Product, and partner teams across Cloudflare. Responsibilities Build behavioral detections . Turn product requirements and available telemetry into production-ready detections across signals such as Access, Gateway, DNS, device posture, and data protection. Define the data dependencies, evaluation approach, and how you will assess signal quality before release. Make risk understandable . Help connect detection output to event history, logs, and customer-facing investigations so practitioners can see what triggered a finding and how a user’s risk changed. Design & Scale Infrastructure : Architect, deploy, and maintain robust distributed systems, focusing on high availability, low latency, and horizontal scalability. Collaborate across teams . Collaborate with Product and engineers in Access, Gateway, Analytics and Reporting, Logs, and other data-producing or data-consuming teams to agree on event contracts and deliver end-to-end functionality. Ensure Reliability . Participate in architecture reviews, capacity planning, and proactive system health monitoring to guarantee maximum uptime and resilience. Desirable Skills, Knowledge, and Experience 3+ years of professional experience building, testing, and operating backend or distributed systems in production. Strong proficiency in at least one systems programming language (e.g., Go, Rust, C/C++) with a track record of writing clean, maintainable code. 3+ years of practical experience with databases and data-intensive services—for example, PostgreSQL, ClickHouse, or comparable relational or analytical stores. Experience reasoning about event-driven systems: message delivery, duplicate or late events, state, failure recovery, and safe changes to live consumers or APIs. Clear written and verbal communication, and an ability to work through technical decisions with product and engineering partners. Experience using metrics, logs, traces, profiling, and experiments to understand production behavior and validate improvements Bring strong opinions, clear judgment, and a willingness to revise your view when data, users, or production reality prove otherwise. Bonus Points Experience with security analytics, fraud or abuse detection, identity, Zero Trust, SASE, or network telemetry. Familiarity with Kafka or other streaming systems; Kubernetes; and high-volume event processing. Experience with statistical baselines, anomaly detection, time-windowed aggregation, or evaluating false positives and detection coverage. Familiarity with DNS, HTTP, authentication events, data-loss-prevention signals, or endpoint-security integrations. Experience with distributed state, caches, object storage, or safe migration of a legacy production service. Compensation Compensation may be adjusted depending on work location and level. New York and Seattle Estimated Base salary $150,000 - $206,000. Austin Texas Estimated Base salary $136,000 - $187,000. What Makes Cloudflare Special? We’re not just a highly ambitious, large-scale technology company. We’re a highly ambitious, large-scale technology company with a soul. Fundamental to our mission to help build a better Internet is protecting the free and open Internet. Project Galileo : Since 2014, we've equipped more than 2,400 journalism and civil society organizations in 111 countries with powerful tools to defend themselves against attacks that would otherwise censor their work, technology already used by Cloudflare’s enterprise customers--at no cost. Athenian Project : In 2017, we created the Athenian Project to ensure that state and local governments have the highest level of protection and reliability for free, so that their constituents have access to election information and voter registration. Since the project, we've provided services to more than 425 local government election websites in 33 states. 1.1.1.1 : We released 1.1.1.1 to help fix the foundation of the Internet by building a faster, more secure and privacy-centric public DNS resolver. This is available publicly for everyone to use - it is the first consumer-focused service Cloudflare has ever released. Here’s the deal - we don’t store client IP addresses never, ever. We will continue to abide by our privacy commitment and ensure that no user data is sold to advertisers or used to target consumers. Sound like something you’d like to be a part of? We’d love to hear from you! Please note that applicants who progress to the offer stage of the interview process may be asked to attend an in-person interview within one of the Cloudflare Offices or Cloudflare Hubs.  More details about this will be available at that stage of the interview process. This position may require access to information protected under U.S. export control laws, including the U.S. Export Administration Regulations. Please note that any offer of employment may be conditioned on your authorization to receive software or technology controlled under these U.S. export laws without sponsorship for an export license. Cloudflare is proud to be an equal opportunity employer.  We are committed to providing equal employment opportunity for all people and place great value in both diversity and inclusiveness.  All qualified applicants will be considered for employment without regard to their, or any other person's, perceived or actual race, color, religion, sex, gender, gender identity, gender expression, sexual orientation, national origin, ancestry, citizenship, age, physical or mental disability, medical condition, family care status, or any other basis protected by law. We are an AA/Veterans/Disabled Employer. Cloudflare provides reasonable accommodations to qualified individuals with disabilities.  Please tell us if you require a reasonable accommodation to apply for a job. Examples of reasonable accommodations include, but are not limited to, changing the application process, providing documents in an alternate format, using a sign language interpreter, or using specialized equipment.  If you require a reasonable accommodation to apply for a job, please contact us via e-mail at hr@cloudflare.com or via mail at 101 Townsend St. San Francisco, CA 94107.

Location: Abu Dhabi, UAE; Kuala Lumpur, Malaysia About Us Established in 2018, Bybit is one of the world’s leading cryptocurrency exchanges and digital financial platforms, serving over 80 million users across more than 200 countries and regions. Powered by world-class technology and a user-first mindset, Bybit delivers a seamless ecosystem across trading, payments, wealth management, custody, institutional services, and Web3 — connecting users to the future of digital finance. Our core values define how we build. We listen, care and improve to create products and experiences that put users first. Backed by a global team of ambitious builders, problem-solvers, and innovators, we foster a high-performance and fast-moving environment where talent is empowered to drive real impact at the global scale. Supported by 24/7 multilingual customer service and a strong commitment to innovation, we are shaping the future of finance through technology, collaboration, and bold execution. Today, Bybit is recognized as one of the most trusted and transparent platforms in the digital asset industry, continuing to expand its global presence while building the infrastructure for the next generation of financial services. Responsibilities Own the product planning and iteration of payment channel integration and infrastructure capabilities, including bank integrations, payment gateways, settlement systems, and internal service frameworks. Design and optimize scalable integration solutions to support multiple payment methods, regions, and partner institutions. Collaborate with engineering teams to build stable, reusable, and extensible infrastructure services that improve integration efficiency and system reliability. Coordinate with external partners such as banks, payment service providers, and vendors to manage integration requirements, testing, launch, and ongoing optimization. Monitor core infrastructure and channel performance metrics, including transaction success rate, latency, system availability, and reconciliation accuracy. Analyze integration failures and operational issues (e.g. API errors, settlement mismatches, callback failures, connectivity issues) and drive root-cause resolution. Define operational processes and contingency mechanisms for channel incidents, failover strategies, and system degradation scenarios. Work closely with compliance, operations, risk control, and customer support teams to ensure infrastructure solutions align with business and regulatory requirements. Requirements 3+ years of experience in payment, fintech, infrastructure, or platform product management. Familiar with payment channel integration processes, API design principles, and distributed system architecture concepts. Understanding of payment lifecycle concepts such as authorization, clearing, settlement, reconciliation, and transaction routing. Strong analytical and problem-solving skills, with the ability to identify system bottlenecks and drive technical optimization initiatives. Ability to communicate effectively with both technical and non-technical stakeholders, and independently drive cross-functional projects. Strong ownership mindset with experience managing complex system integrations and infrastructure-related initiatives. Preferred Qualifications Experience with banking integrations, payment gateways, or acquiring systems. Experience in cryptocurrency exchanges, cross-border payments, or fintech infrastructure. Familiarity with service reliability, monitoring systems, or transaction routing strategies. Basic understanding of risk control, compliance, and operational processes in payment systems. Why Join Us At Bybit, we are committed to fostering a supportive and enriching work environment. Our benefits include: - Study Growth Fund: We support your professional development and continuous learning. - Internal Events: Participate in regular team-building activities, workshops, and events designed to promote collaboration and innovation. - Global Collaboration: Be part of a diverse, international team, working alongside colleagues from around the world. - Career Advancement: Access opportunities for growth and advancement within a rapidly expanding global company. - Internal Mobility: Grow with us- Your long-term development is important to us. We offer internal job opportunities to help build your career path.

Location: Yerevan, Armenia Role Purpose The AI Product Stream Lead defines and delivers AI-powered supply chain solutions that solve real customer and operations problems on the Cargoo platform. You own a product stream end to end, from framing where AI creates value (automation, decision support, agents) to shipping it and proving it works in production. You lead a cross-functional team, balancing customer value, technical and data feasibility, AI quality and cost, business priorities and delivery timelines. You also help set how Cargoo builds product in an AI-native way. Key Responsibilities AI Product Vision & Strategy Shape the stream’s product vision and strategy with stakeholders, in line with Cargoo’s AI strategy and customer needs. Identify and prioritize AI opportunities across freight operations (e.g. exception handling, quoting, document processing, planning and execution support) by value, feasibility and risk. Decide where AI should automate, where it should assist, and where a human must stay in control. Own product decisions, balancing customer value, technical feasibility, AI quality, cost, business priorities and delivery timelines. Leadership & Delivery Lead the development team through the whole product lifecycle, from discovery to rollout and adoption. Decide scope, schedule and quality, including when an AI feature is good enough to ship and when it needs another iteration. Run fast discovery loops: prototype with AI tools, validate with real users and data, then commit. Make sure the team meets internal policies, data protection rules and responsible-AI standards (incl. EU AI Act awareness). Product Management & Requirements Create clear Use Cases, Requirements and Business Rules. For AI features, add expected behaviour, edge cases, failure modes and escalation paths. Define success and evaluation criteria for AI features with engineering: accuracy, latency, cost per task and acceptable error rates, backed by representative test sets. Write User Stories and Acceptance Criteria that cover both deterministic logic and probabilistic AI behaviour. Work with engineering and data owners on data readiness: which data we need, its quality, and who owns it. Own and continuously refine the Product Backlog so the team always works on the highest-value opportunities. Track adoption and impact after release (time saved, automation rate, user trust) and feed the learning back into the roadmap. AI-Native Ways of Working Use AI tools every day for research, specs, prototyping, data analysis and feedback synthesis. Help the team and wider product org adopt AI-assisted delivery practices. Candidate Profile Education & Experience 5+ years managing software development projects, strongly preferably as a Product Owner. Proven managerial skills: team leadership, decision-making and conflict resolution. Degree or diploma in Information Technology, Informatics or a related field. Technical Skills (Must Have) Deep understanding of Agile values, principles and frameworks (Scrum, Kanban, Lean) and the ability to apply them well. Strong analytical skills, comfortable working with data to make product decisions. Solid working understanding of how LLMs and AI agents work, what they are good at, and where they fail (hallucinations, non-determinism, cost, latency). Hands-on, daily use of AI tools in your own work. Fluent English, written and spoken. Strong Plus Having shipped at least one AI/ML or LLM-based feature to production. Experience defining evaluation criteria or test sets for AI features. Logistics background or experience with logistics solutions. Experience with data products, workflow automation or decision-support systems. Behavioral Traits Excellent communication with technical and non-technical audiences, including customers, executives, managers and subject matter experts. Able to explain AI trade-offs in plain language. Strong leadership and decision-making, able to guide teams and resolve conflicts. Analytical, structured and value-driven in prioritization. Evidence over hype. Curious and fast-learning, comfortable with ambiguity and quick iteration.

Location: Almaty, Kazakhstan Workplace: OnSite WHY WORK AT HIGGSFIELD AI? Higgsfield AI is the fastest-scaling generative AI company in history, hitting $1B in annual revenue run rate, 30M+ users worldwide, 6M+ generations per day, and powering 390 of Fortune 500 brands. We're building at the absolute frontier of AI-powered video creation and next-generation creative tools. Joining Higgsfield means becoming part of a high-impact team shaping the future of AI-native experiences, at a company that isn't just moving fast, but rewriting what fast looks like. WHAT THIS ROLE MEANS AT HIGGSFIELD We sell subscriptions and credit packs to consumers in over a hundred countries, and today almost all of it moves as cross-border card payments in USD. That has three consequences, and this role owns all three. We are declined more than we should be. In some of our largest growth markets roughly one attempt in three fails, and a meaningful share of those are not "no money" — they are cross-border blocks, card limits, stale credentials on renewal, and issuers who do not recognise us. We pay more than we should. Our blended cost of acceptance is the single largest line below compute, and the majority of the network component sits on transactions our processor classes as cross-border. We are not on the rails our customers use. In two of our fastest-growing markets, the dominant local payment method is one we do not offer at all. You own the measurement and the argument behind fixing all of it. You make sure: - We know our true authorization rate — at the unit the business actually cares about, not the one that is easy to compute - Every basis point of payment cost has a name, a geography and an owner - A decline is a diagnosis, not a statistic - When we propose a new rail, a new entity or a new processor arrangement, the number underneath it holds up to Stripe's own analysis and to our board's WHAT YOU WILL DO Authorization & decline analysis - Own authorization rate end to end: by country, issuer, BIN, card brand and funding type, plan price point, first payment vs renewal, and payment method. - Build and maintain the decline taxonomy — what each decline code actually means, which are recoverable, which are terminal, and which are us rather than them. - Measure at the right unit. An invoice that succeeds on the third retry is a success, not two failures and a success, and reporting it the other way has made teams optimise the wrong thing. - Separate soft declines we can recover from hard declines we cannot, and put a number on the recoverable pool. Cost of acceptance - Own the blended take rate and its decomposition: interchange, scheme fees, processor markup, cross-border and currency-conversion components, disputes, and the fixed per-transaction pieces. - Find where the cost actually sits. Averages hide it — the cost is concentrated by corridor, by card type and by whether the transaction is domestic. - Build the evidence for commercial conversations with our processor, and keep it current enough to reuse. Local payment methods, entities and routing - Size the case for new rails market by market: what share of local commerce runs on them, what our current card performance costs us, and what we would realistically recover. - Model merchant-of-record and local-acquiring arrangements against the alternative of our own entities, honestly — including the fee we would pay for the privilege. - Design and read the experiments that prove it, with the product analytics team. A new payment method is an A/B test, not a launch. Renewals, dunning and involuntary churn - Own the retry and dunning strategy as a measurable system: when to retry, how often, through which rail, and when to stop. - Quantify involuntary churn separately from voluntary. They look identical in a cancellation count and they need completely different fixes. - Track the health of stored credentials — network tokens, card-account-updater coverage, expiring cards — because in a subscription business that is silent revenue leakage. Disputes, refunds and reconciliation - Own dispute and chargeback rates against network thresholds, with the lag handled properly — disputes arrive weeks after the charge and a same-period denominator understates you. - Reconcile processor data to the revenue ledger and explain the differences rather than plugging them. - Work with the antifraud team where disputes are abuse rather than genuine dissatisfaction. HOW WE WORK - SQL and Python on raw processor and billing data in BigQuery. We use AI heavily — resourcefulness beats syntax. - Partner with Finance (the numbers get booked), Product (checkout and paywall), Engineering (the billing integration) and our processor's team directly. WHO WE'RE LOOKING FOR - 2+ years in payments analytics, payment operations, billing, or risk at a merchant, PSP, acquirer, or fintech. This is a domain role — general analytics experience without payments does not transfer quickly enough. - Fluency in the actual mechanics: authorization vs capture vs settlement, decline codes, 3DS and SCA, network tokens and account updater, interchange and scheme fees, chargeback lifecycle, MCC, BIN, cross-border and DCC. - Strong SQL, without help. Most of this job is joining sessions to intents to charges to invoices to refunds across imperfect keys and getting the denominator right. - Python at working level for analysis. - The instinct to check the unit of measurement before quoting a rate — per attempt, per invoice, per user, per session are four different numbers and only one of them answers the question. - Comfort with subscription billing specifically: renewals, proration, plan changes, failed payments, dunning, involuntary churn. - Commercial judgment: you can build the case for a rail or a rate and defend it against a vendor who does this for a living. - Clear written and spoken English, B2+. Backgrounds that often do well: - Payment operations or payments analytics at a consumer subscription, marketplace or gaming company - Analysts from PSPs, acquirers, orchestration platforms or card networks - Billing / revenue operations analysts who owned dunning and involuntary churn - Risk or chargeback analysts who moved toward the acceptance side WHAT THIS ROLE IS NOT This role is not a fit if you: - Want to own checkout UX or pricing — that is Product Analyst, and it is open - Want to build fraud models — that is the Antifraud data scientist, and it is open - Would report an authorization rate without saying what the denominator was - Would take a processor's own analysis of their own performance at face value - Need someone else to prepare the data - Want predictable 9–5 workdays HIRING PROCESS - Screening call (30 min) - Hiring-manager screen (30 min) - Business case (1 hour) - Take-home with real-shaped data (7 days, ~10–12 hours of work) - Team interview (60 min) - Paid on-site trial (1 month) THE DEAL - Competitive salary in USD - Equity: participation in the company's stock option program - On-site role in Almaty, Kazakhstan - Relocation support (flight + temporary housing) - Flat structure, high autonomy, fast career growth

Responsibilities: — Design, create, and maintain automated test scripts (UI & API). — Integrate test automation suites into CI/CD pipelines. — Analyze test results, investigate failures, and report bugs clearly. — Work closely with developers and manual QA engineers to expand automation coverage. Key Requirements: — 3+ years of hands-on experience in QA Automation. — Proficiency in Python or JavaScript / TypeScript (or Java). — Strong experience with UI automation frameworks (Selenium, Playwright, or Cypress). — Hands-on experience with API testing and automation (Postman, REST Assured, or requests). — Understanding of basic testing concepts, methodology, and software development lifecycle (SDLC). — Familiarity with Git version control. — Basic understanding of CI/CD tools (GitHub Actions, GitLab CI, or Jenkins). Conditions: — Location: Remote within EU (must hold EU Citizenship, PR, or a valid EU Work/Residence Permit). — Type: Full-time / B2B Contract. — Fluent English (B2+/C1) is required.

Location: Remote Workplace: Remote About Prefect Prefect builds automation for an unpredictable world. The last decade of automation was about protecting workflows from unpredictability; in the agentic era, unpredictability is the point. Our mission is to give people confidence in automated work, whether that work is a mission-critical data pipeline or a fleet of AI agents. In August 2026, Prefect and Dagster, competitors for eight years, joined forces to build the next generation of automation infrastructure. Today, our ecosystem includes Dagster, the data platform; Prefect, the agent platform; and FastMCP, the leading open-source developer framework for the MCP ecosystem. Together, our open-source and commercial products are trusted by Fortune 500 companies, data innovators, and high-growth technology companies around the world. We think of our culture as the operating system of the company. We’ve carefully created a supportive, high-performance environment that empowers our team to do the best work of their careers, have meaningful impact, and continue growing personally and professionally. We’re a remote-first team that values high standards, ownership, and thoughtful collaboration. ROLE SUMMARY Developer Success is the technical support function for Prefect's customers across the Prefect and Dagster product lines. Its mission is to resolve customer technical issues with the least possible customer effort, and to prevent those issues from recurring. We are hiring a Developer Success Engineer to work the combined Prefect and Dagster support queue. You will diagnose and resolve technical issues in customer environments, own each issue through resolution, and contribute to the documentation, tooling, and product feedback that reduce future volume. You will be trained on both Prefect’s and Dagster’s products. The team is investing in agent-based automation for routine support work, which is moving the engineering focus toward complex diagnosis and escalation. This role is well suited to an engineer who wants to build deep expertise in orchestration and data platform environments and apply it directly with customers. WHAT YOU'LL DO - Clear the failure state, and prevent the next one. Diagnose problems across Prefect and Dagster installations, integrations, deployments, and the cloud and container environments they run in. Find the underlying "why" so your first response resolves the question and pre-empts the follow-ups. Turn what you learn into docs, debugging resources, and tooling that deflect repeat submissions. - Own what's ours; say no clearly to what isn't. Take full accountability for root causes inside our products' domain of control. For problems outside our Scope of Support, issue a principled no that still gives the customer a way forward. Helplessness erodes trust as fast as a wrong answer. - Support your findings, and ask customers to support theirs. Back every diagnosis with evidence and logic. Assume the customer is the expert on their own stack, ask for what you need to troubleshoot both sides of an issue, and explain why you need it. Leave room for the possibility you've missed something. - Give the best possible update every time, and say so when you're still building it. A half-baked answer sent fast is worse than a complete one sent when it's ready; but radio silence is worse than both. When an issue needs to be escalated, you stay the customer's single point of contact, keep them updated on a regular cadence, and close the loop. - Feed the product. File reproducible product issues and surface recurring failure patterns to Engineering and Product. You'll work the queue independently on day-to-day issues and own moderate-complexity problems end to end, including investigations across a customer's cloud environment. You'll ask when the Scope of Support or an escalation path is unclear, and you'll build the judgment to recognize when an issue belongs with Customer Engineering or a CSM instead. We measure the role on Customer Effort Score and resolution time, held in tension: a fast answer that costs the customer effort isn't a good one. Coverage is 9am to 5pm Pacific, with an on-call rotation shared across the team. YOUR QUALIFICATIONS Must have: - Python fluency at the level of reading and debugging someone else's code, not only writing your own. - Hands-on experience running workloads in at least one major cloud and in containers or Kubernetes, enough to troubleshoot a deployment you didn't build. - Experience with a workflow orchestrator (Prefect, Dagster, Airflow, or comparable) and a strong interest in the data engineering space it lives in. - A track record of technical, customer-facing work: support, solutions engineering, developer relations, or similar. - Clear enough written communication to explain a root cause to a skeptical senior engineer and earn their trust. Nice to have: - Direct experience with Prefect or Dagster specifically. - A history of open-source contribution, in any project. - SQL and data warehouse familiarity. - Experience building support tooling or automation. WHAT TO EXPECT This is a support role. There's a queue, a Scope of Support, and an on-call rotation, and the first job is always the failure state in front of you. If a defined scope and a real customer on the other end of every problem sounds like the point, this is the role. While engineers on this team do contribute code and take on consultative work, the queue comes first. Those opportunities will progressively open up as the agent tooling absorbs the formulaic work. WHAT YOU'LL GET IN RETURN Base Salary Ranges: - San Francisco Bay Area & NYC Metro: $149,000–$176,000 - Washington D.C., Boston, LA Metro, Seattle: $136,000-$165,000 - Denver, Chicago, Atlanta, & all other U.S. metros: $128,000–$150,000 Actual base salary within each range depends on the candidate's location, experience, and qualifications. Please keep in mind that equity and benefits are not included in the range provided above. Your recruiter can share more about the salary range for your preferred location during the hiring process. Perks and Benefits: - Remote-first team with flexible-first culture - Equity Stock Options - 401(k) with 5% company match (vests immediately!) - Unlimited PTO - Medical, Dental and Vision insurance - Generous Parental Leave - Life Insurance and Disability benefits - $800/month remote work stipend And that's just the start — for more info check out our benefits and perks on our careers page. BELONGINGNESS AT PREFECT: Belongingness at Prefect is our commitment to ensuring mutual power, access, and opportunity among all groups and individuals who make up our community. We operationalize belongingness through actions large and small on a daily basis, but always with the goal that all groups and individuals can contribute as their most authentic self to the evolution of our product, our team, and our community. Belongingness is also our commitment to bridge with individuals or groups not yet established in our community, acknowledge differences in a way that affirms distinct identities, and allows for a new, more expansive group identity to form. EQUAL OPPORTUNITY AT PREFECT: Prefect is an equal opportunity employer and encourages applications from people of all backgrounds. All applicants will be considered for employment without attention to race, religion, color, sex (including pregnancy, sexual orientation and gender identity/expression), national origin, disability or any other status protected under applicable federal, state, or local laws. Delaney is a real live person (👋🏻) and is looking forward to learning more about you through your application.

Location: Paris, France Workplace: Hybrid HEALTH CAN’T WAIT. Not for symptoms to get worse. Not for a six‑month appointment. Not for a system to catch up. But that’s exactly how healthcare works today. You wait, until you can’t. Alan exists to end the wait. Health is a universal right, and we believe this right can only become real when it’s coupled with prevention. We need to stop treating health as something we repair and start treating it as something we build, every day. It’s not solely a question of willpower. It’s the healthcare system itself that needs to work for everyone, in a sustainable way. So we are building the new standard in prevention insurance. Alan is the first company that integrates insurance, prevention, and care into a single, acclaimed user experience. We are on an incredible journey to build a global leading company, with a unique culture https://alan.com/en/careers. We already partner with 40K+ companies of all sizes, serving more than 1M+ members, and have reached €800M+ in ARR. Prevention as the new norm. That's what we're building with our team of 1000+ people. If it speaks to you: we're hiring across France, Spain, Belgium, and Canada. And beyond. MISSION Alan is scaling internationally (800+ employees, 100+ healthcare professionals, 5+ countries) and needs IT Engineers to build and own the internal technology platform every Alaner works on: identity and provisioning, endpoint and SaaS security, the tooling behind our clinics, and the automation that keeps IT from scaling linearly with headcount. The scope below is what the team owns, not a checklist for one person. You'll be staffed on one or two of these areas at a time, according to your strengths and interests, and it will evolve through time based on company needs and personal appetite. Staying close to users keeps us building the right things: you'll spend up to 2-3 days a month on IT support, seeing first-hand where friction lives and what's worth automating next. SCOPE ⚙️ PLATFORM & AUTOMATION - Own and extend automations on Alan Home, our internal provisioning engine - Maintain, extend and improve helpdesk automation, SaaS provisioning flows, and onboarding/offboarding pipelines - Own the meeting room AV and room-booking platform as a product, replicable across offices 🔑 IDENTITY & ACCESS - Own the IT side of the employee lifecycle (onboarding, offboarding, mobility between jobs and countries) across Google Workspace, SSO/SAML and our SaaS ecosystem. - Grow our role-based access model: extend it to more tools and refine the permission groups, so access is granted by role rather than case by case - Make employee lifecycle events reliable, fast and auditable end-to-end - Own access reviews and least-privilege models; produce and maintain the evidence our auditors rely on - Own SaaS procurement and license management: vendor relationships, renewals, and cost optimization across our stack 🔒 ENDPOINT & SAAS SECURITY - Own the Mac fleet in Kandji: enrollment at scale, role-based blueprints, fleet compliance - Cloudflare WARP, SaaS security controls - Maintain and strengthen our ISO 27001 certification and drive ACPR and pentest remediation on the endpoint and platform scope 🩺 HEALTH PROFESSIONALS IT - The IT layer behind Alan's clinics: practitioner tooling, equipment, access - Role and permission automation for healthcare professionals, and their support and escalation process 🌍 INTERNATIONALISATION - Make international expansion something IT accelerates, not something it slows down: a repeatable process where every new market (Canada, KSA, Armenia...) launches on our standard stack from day one - Bring acquired entities onto that same platform MILESTONES First 3 months - Acquire technical knowledge on Alan's platform: Alan Home, Google Workspace/SSO, Kandji, Cloudflare WARP - Take one project end-to-end and ship a first increment - Join the IT oncall team as technical escalation First 6 months - Own a workstream end-to-end - Deliver one automation or hardening initiative end-to-end - Take up documentation and automation on your scope and reduce significantly the bus factor on it - Mentor IT teammates on your core expertise First year - Deliver measurable IT toil reduction on your scope - Set the technical standards on your domain: how we build, document and review it - Build strong cross-functional partnerships (Engineering, Security, Workplace, Clinic, People, Audit) - Contribute to hiring and onboarding new teammates 3 years - Lead major projects end-to-end across several communities - Be the reference the team relies on across at least two domains - Mentor the IT team and beyond, and scale IT without a linear increase in manual work As the community grows, there is room to take on more responsibility: leading a crew, owning cross-community projects, mentoring, hiring. WHAT WE'RE LOOKING FOR Must-have - Technical/hard skills: - 5+ years in IT engineering, platform, systems or corporate engineering, with real ownership of production-facing systems - Depth in at least one area (platform & automation, identity, endpoint & security) and demonstrated range beyond it. - You write code: not necessarily as a full-time software engineer, but Python, APIs and pull requests in a shared repo should feel normal - Structured problem-solving: diagnoses methodically, asks the right clarifying questions, thinks about failure modes and rollback - Comfort with regulated environments: health data, ACPR, ISO 27001, HDS - Automation & AI appetite: uses AI tools smartly to accelerate their own delivery, with an instinct for what should be automated or delegated to AI rather than repeated Must-have - Behavioral / soft skills: - Ownership & autonomy: drives topics forward without hand-holding, including when they cross into someone else's perimeter - Written communication: Alan is written-first; decisions get argued in public documents and challenged by people outside your team - Pragmatic judgment: balances "perfect" vs. "good enough", decides with incomplete information - Documentation culture: reduces bus factor through clear documentation and knowledge sharing Nice-to-have skills / Bonus points if you have: - Experience with an internal provisioning or IdP-driven automation platform - Kandji or another MDM at scale - Cloudflare Zero Trust / WARP, Datadog - Audit remediation experience (ISO 27001, HDS) - Multi-country rollouts, or IT integration after an acquisition - Having mentored, coached or led a (small) crew - Health or healthtech ecosystem familiarity Languages: Fluent English & Professional French

Location: Amsterdam, Netherlands; Berlin, Germany; London, United Kingdom; Poland; Prague, Czech Republic; Tel Aviv, Israel; Zurich, Switzerland About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role Nebius Token Factory is looking for scientists who can turn frontier LLM and VLM inference bottlenecks into research questions, conduct rigorous experiments, and translate their findings into production capabilities. You will own focused research and optimization projects, write strong code, and produce prototypes, publications, and technical artifacts that engineers can build on. Your research will span quantization, distillation, speculative decoding, KV-cache optimization, and model/runtime co-optimization, alongside inference engines and distributed inference architectures. You will investigate how these approaches interact, evaluate their trade-offs across model quality, latency, throughput, memory footprint, and cost per token, and work with engineering teams to bring promising results into production. Your responsibilities Lead research projects in efficient LLM and VLM inference, from hypotheses and experiments through ablations, prototypes, and production handoff. Develop and evaluate methods spanning quantization, quantization-aware training (QAT), distillation, speculative decoding, KV-cache reuse, KV-cache compression, long-context inference, MoE routing, and model/runtime co-optimization. Build prototypes using PyTorch, Triton, CUDA-adjacent tooling, or inference-serving frameworks, and collaborate with MLEs and platform engineers to turn them into production components. Research LLM request routing and scheduling strategies, including cache-aware load balancing and prefill–decode disaggregation (PDD). Evaluate how queueing, KV-cache transfer, worker placement, and prefill/decode capacity allocation affect latency, throughput, and serving cost. Investigate multi-node inference for dense and mixture-of-experts models, including wide expert parallelism (WideEP). Study expert placement, load imbalance, and computation/communication trade-offs, and use the findings to guide model and system architecture choices. Develop rigorous evaluations covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token. Compare serving architectures under consistent workload conditions and GPU budgets. Work with MLE, GPU kernel, backend infrastructure, product, and customer teams to select research priorities with measurable production impact. Share results through internal reports, technical blogs, papers, and open-source artifacts, and mentor engineers and scientists on experimental design, scientific rigor, and model/system trade-offs. Must-haves A PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied mathematics, or a closely related discipline. A strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas. Strong Python and PyTorch implementation skills, with the ability to turn ideas into experiments and working prototypes. Deep knowledge of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving trade-offs. Strong experimental design skills covering ablations, baselines, metrics, statistical reasoning, and failure analysis. Excellent written and verbal communication. Nice-to-haves First-author publications at venues such as NeurIPS, ICML, ICLR, MLSys, ACL, EMNLP, ASPLOS, OSDI, SOSP, ISCA, or HPCA. Experience deploying ML models or inference optimizations in production. Experience with vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, FlashAttention, FlashInfer, Triton, CUDA, or PyTorch internals. Experience applying post-training, SFT, DPO, RLHF, RLAIF, preference optimization, or synthetic data generation to inference quality or efficiency. Open-source research artifacts, widely used benchmarks, technical blogs, or invited talks demonstrating contributions to efficient AI systems. Research or implementation experience in one or more areas of distributed inference system architecture, such as LLM request routing, PDD, multi-node serving, or MoE expert parallelism, including WideEP. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Location: San Francisco, CA Workplace: Hybrid About Niantic Spatial We are building for the >80% of economic activity that happens beyond the reach of screens. AI has learned from the text, images and video online, but it still lacks an accurate understanding of physical space. That’s one of the biggest unsolved problems in AI, and it’s the one we’re solving at Niantic Spatial. We build spatial AI models and services so people and machines can reason, learn and act safely in the physical world. We turn camera imagery, video, sensor data and archives into accurate, AI-readable representations of real places that robots can train in and machines can use to know exactly where they are. In physical AI, mistakes have real consequences. That’s why we build AI grounded in measured reality, not guesswork, so the people and machines relying on it can trust it to get things right. Our team built some of the world’s most widely used maps. Now we’re building something bigger: a living, measured record of the real world that machines can query, learn from and use to understand how places change over time. If you want to help AI understand the world we actually live in, come build with us. About the Role Niantic Spatial makes the physical world computable, helping people and machines collaborate safely by aligning how they understand reality. One of the fundamental problems we're helping autonomy teams and engineers overcome is the sim-to-real gap for visual-spatial understanding. We're applying our team's decades of experience encoding the world precisely as it is at Google Maps, Google Earth, and Niantic Labs to build the scalable real-to-sim stack for embodied AI. We're looking for a Senior/Staff Product Engineer to join our Embodied AI team. Working alongside the group's GM, Product leader, and robot learning engineer, you'll own how the product reaches customers, build the integrations that make it native to their simulators and deployment stacks, and influence the roadmap. You've turned capabilities into products. You've shipped developer-facing products and watched adoption turn on details nobody planned for. You know that a capability isn't a product until someone outside the building has used it without help, and you're passionate about the work that sits between the two. This is not a role for someone who wants to manage and not ship, or someone who wants to ship without ownership of commercial consequences. This is a role for an entrepreneurial engineer who loves to build and is energized by making the people around them, researchers and customers alike, more effective. You have a co-founder mentality. You may have been part of a founding team before, or want to start your own business one day. What You'll Do - Build the Product Surface - Own how autonomy teams experience our work: the APIs, UIs, packages, and documentation. - Make Our World Native to the Customer's - Turn scans and reconstructions into reliable, interoperable assets that work in customers' simulation tools and workflows, including packaging, metadata, semantic labels, object structure, and selectable variants. - Turn Prototypes Into Capability - Partner with reconstruction, mapping, localization, and research teams to transform prototypes and pipeline outputs into production-grade services and user experiences. - Deliver at Scale - Design and operate backend systems for accounts, organizations, sites, scans, assets, processing jobs, permissions, asynchronous workflows, storage, and data access. - Make It Work - Work directly with customers and internal users to integrate our technology, diagnose failures, improve reliability, and close the gap between intended and actual product behavior. - Influence the Roadmap - Work alongside the group's GM, Product leader, and robot learning engineer to shape what we build and when. What You'll Bring - 6+ years of software engineering experience; Bachelor's degree in Computer Science or a related field, or equivalent practical experience. - Significant experience building and operating production web applications and backend services, with strong fundamentals in API design, data modeling, testing, observability, and reliability. - Shipped developer-facing products — including the documentation and reference code external engineers actually used — and worked with 3D asset formats or comparable binary formats to production depth: you can say what the conventions were, where they broke, and what it cost you. - Proficient in Python and TypeScript, comfortable working across frontend and backend, and able to take a feature from product requirements through to running it in production. - Experience designing asynchronous workflows, long-running jobs, external integrations, and data-intensive systems. Nice to Have - Experience working with robotics simulators or toolchains such as Isaac Sim, Isaac Lab, or MuJoCo, or OpenUSD. - Built products for geospatial, 3D, computer vision, graphics, robotics, or spatial computing. - Worked on visualization or asset-management experiences with React, Next.js, or Three.js. Competencies - Relentless bias for action. You make decisions and ship as if the company’s success depends on it, because it does. You set the pace, and drive the people around you to rise to it. - Intellectually honest. You know there’s just one job that underpins every other in a startup: find the truth. You are relentless in your pursuit of it, including when it challenges your own convictions, and you raise concerns when you see them — even when it's uncomfortable, and especially when it's unpopular. - Pragmatic. You have no patience for not-invented-here syndrome and analysis paralysis. You find the fastest path to a working capability without one-way doors that undermine scaling. - Confident and credible with customers. You can read an autonomy engineer's training config, find why our asset didn't load, and explain the fix in their terms. - Fluent in other people's ecosystems. You'll spend much of your time learning new tools and stacks. You're quick to learn the customer's language, and you can point to something you shipped in a world that was foreign to you a quarter earlier. Compensation & Benefits The base salary range for this role is $207,000 - $265,000, and your offer within it will reflect your experience and the scope you take on. Compensation also includes an annual bonus, equity, and a comprehensive benefits package including medical, dental, and vision coverage, 401(k), and more. Location & Work Model This role is based in our San Francisco office, with three days per week in office. Inclusive Application We know the strongest candidates don't always tick every box. If you're excited about this role and believe you could do it well, we encourage you to apply even if your experience doesn't match every qualification listed - you may be exactly who we're looking for. Equal Opportunity Niantic Spatial is an equal opportunity employer. Individuals seeking employment at Niantic Spatial are considered without regard to race, color, ancestry, national origin, religion, creed, age, gender (including pregnancy, childbirth, breastfeeding or related medical conditions), marital status, physical or mental disability, medical condition, genetic information, military or veteran status, gender identity, gender expression, sexual orientation, or any other protected category under applicable laws. Niantic Spatial will also consider qualified applicants with criminal histories in accordance with applicable laws. Please contact your recruiter if you want to request an accommodation for the job application or interview process. Candidate Privacy I understand that by submitting my job application, the information I provide as part of that application will be used in accordance with Niantic Spatial's Privacy Notice for Job Applicants and Candidates https://www.nianticspatial.com/applicant-privacy-notice.

Location: Bengaluru, India We are looking for a Senior AI Quality Engineer (L3+) with strong Python skills and hands-on experience testing AI and ML systems. You will analyze requirements, define evaluation strategies, build automated checks, validate models and outputs, test APIs and data pipelines, verify defects through resolution, and contribute to release readiness for your assigned product area. You will both evaluate AI systems and design, build, and operate QE agents for the software testing life cycle. AI-assisted work is part of everyday QE practice on this engagement, with Ragas, DeepEval, promptfoo, Cursor, and other AI and ML tools used for evaluation, scripting, test generation, analysis, and maintenance. You will also build golden datasets, calibrate LLM-as-judge methods, test agentic workflows and RAG systems, implement self-healing and intelligent test approaches, and diagnose quality, safety, performance, and cost failures across distributed AI services. This position is intended for Hyderabad-based candidates who are available to work from the office. The client delivers AI agents, retrieval systems, models, and data pipelines across a broad technology landscape. Quality is part of delivery through evaluation gates, automated regression, model and data validation, security and privacy checks, and evidence that a release is ready. Python is the primary language for AI evaluation, model validation, API testing, data-pipeline quality, and QE agent design. The role combines deterministic automation with probabilistic evaluation to assess AI behavior across models, data, retrieval, tools, and integration surfaces, and designs and operates agents that support the software testing life cycle. Analyze requirements and define evaluation strategies, golden datasets, regression benchmarks, baselines, thresholds, and blocking metrics with domain-expert review. Design, build, and orchestrate AI agents, persona-based workflows, and reusable skills for the STLC using LangChain, LangGraph, or Semantic Kernel. Build automated evaluations and benchmarks for LLMs, agents, RAG, retrieval, tool trajectories, and contracts using Ragas, DeepEval, and promptfoo. Calibrate LLM-as-judge against human grading, manage non-determinism through repeated runs, and detect regression and drift. Write and maintain Python and pytest automation for AI, ML, API, data, and integration workflows. Use Cursor and agentic tools to analyze requirements, generate and review tests, and maintain evaluation suites. Validate deep-learning, reinforcement-learning, and predictive models using LangChain. Build AI-assisted STLC workflows for requirements analysis, test design, script generation, execution, self-healing, failure analysis, and defect triage, plus intelligent test selection and defect prediction. Integrate LLMs, APIs, embeddings, vector databases, retrieval pipelines, and enterprise data sources into QE frameworks using Python. Test REST and streaming APIs, agent tools, and RAG and MCP integrations, including authentication, errors, retries, and recovery. Test data and retrieval pipelines for schema integrity, lineage, idempotency, fallback, promotion gates, and rollback. Integrate evaluations, benchmarks, and agent workflows into QE frameworks and CI/CD on GitHub Actions, review Allure results, and keep TestRail aligned with automated coverage. Test prompt injection, jailbreaks, unsafe tool use, data exfiltration, access control, PII controls, and guardrails. Measure accuracy, relevance, groundedness, hallucination, retrieval quality, agent reliability, latency, throughput, token use, and cost, and diagnose distributed failures. Implement tracing, monitoring, and production evaluation using Langfuse, LangSmith, or a comparable AI observability platform. Deploy and operate QE agents on Amazon Bedrock AgentCore or a comparable agent platform, with governance and human oversight. Deploy and validate AI and ML solutions on AWS or Azure, including secrets, database services, and cloud-native test infrastructure. Report, track, and verify defects in Jira, and work with domain experts, product, and engineering on traceable coverage and untested scope. Contribute to release readiness for the assigned product area without owning sign-off for the whole team. Strong Python proficiency with pytest for test and evaluation automation. Hands-on experience building and orchestrating AI agents and agentic workflows with LangChain, LangGraph, Semantic Kernel, or comparable frameworks. Hands-on experience building AI evaluations and benchmarks with Ragas, DeepEval, promptfoo, or equivalent tools. Hands-on use of Cursor or agentic development tools for test generation and maintenance. Experience defining AI evaluation strategies, golden datasets, regression benchmarks, and LLM-as-judge calibration, including non-determinism and drift. Experience validating deep-learning, reinforcement-learning, and predictive models with LangChain. Experience building AI-assisted STLC workflows for requirements analysis, test design, script generation, execution, self-healing, failure analysis, defect triage, intelligent test selection, and defect prediction. Experience integrating LLMs, APIs, embeddings, vector databases, and retrieval pipelines with automation frameworks in Python. Experience testing REST and streaming APIs, agent tools, RAG workflows, and MCP integrations. Experience testing data and retrieval pipelines for correctness, lineage, and safe promotion. Experience integrating AI evaluation and agent workflows with automation frameworks and CI/CD using GitHub Actions, Allure, TestRail, or equivalent tools. Experience testing AI security, privacy, access control, and guardrails. Experience measuring accuracy, relevance, groundedness, hallucination, retrieval quality, agent reliability, latency, throughput, token use, and cost, and diagnosing distributed failures. Experience with AI tracing, monitoring, and production evaluation using Langfuse, LangSmith, or comparable tools. Familiarity with AWS, Azure, or GCP cloud AI services and agent platforms for deploying and validating AI and ML solutions. Experience deploying and operating agents on Amazon Bedrock AgentCore or a comparable agent platform, with governance and human oversight. Experience managing defects through resolution in Jira or an equivalent tool. Ability to work with domain experts, product, and engineering on ground truth and coverage gaps. Experience contributing to release readiness for an assigned product area. Playwright and TypeScript experience for UI and API automation. Real-device and cross-browser execution using LambdaTest, Sauce Labs, or an equivalent platform. Knowledge of AI-driven test optimization and coverage analytics using SeaLights or an equivalent platform. Experience contributing reusable components to shared automation frameworks and reviewing automation code through Git and GitHub workflows. Experience managing test data and troubleshooting test environments needed for reliable execution. Experience triaging security findings and validating operational readiness, monitoring, rollback, and recovery controls. Experience producing audit-ready evaluation reports and improving shared evaluation patterns, automation libraries, and datasets.