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Location: San Francisco, CA | Seattle, WA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role The Cloud Inference team scales and optimizes Claude to serve the massive audiences of developers and enterprise companies across AWS, GCP, Azure, and future cloud service providers (CSPs). We own the end-to-end product of Claude on each cloud platform, from API integration and intelligent request routing to inference execution, capacity management, and day-to-day operations. Our engineers are extremely high leverage: we simultaneously drive multiple major revenue streams while optimizing one of Anthropic's most precious resources: compute. As we expand to more cloud platforms, the complexity of managing inference efficiently across providers with different hardware, networking stacks, and operational models grows significantly. We need product-minded backend engineers who can navigate these platform differences, design the services and abstractions that work across providers, and make architectural decisions that keep us reliable and cost-effective at massive scale. Your work will increase the scale at which our services operate, accelerate our ability to reliably launch new frontier models and innovative features to customers across all platforms, and ensure our LLMs meet rigorous safety, performance, and security standards. Key responsibilities Design, build, and own backend services and infrastructure that serve Claude across multiple CSPs, accounting for differences in compute hardware, networking, APIs, and operational models Work cross-functionally with internal inference, product API, systems, and security teams, among others, and with CSP partners to stand up the full serving stack on new cloud platforms, resolve operational issues, and influence provider roadmaps Build and evolve CI/CD automation systems, including validation and deployment pipelines, that reliably ship new model versions to millions of users across cloud platforms without regressions Design interfaces and tooling abstractions across CSPs that enable cost-effective inference management, scale across providers, and reduce per-platform complexity Contribute to capacity planning, autoscaling, and workload routing strategies that match supply with demand and direct requests to the most cost-effective accelerator and region Analyze observability data across providers to identify performance bottlenecks, cost anomalies, and regressions, and drive remediation based on real-world production workloads Minimum qualifications Have significant software engineering experience, with a strong background in high-performance, large-scale distributed systems serving millions of users Have experience building or operating services on at least one major cloud platform (AWS, GCP, or Azure), with exposure to Kubernetes, Infrastructure as Code, or container orchestration Are curious about LLM serving; prior inference or ML experience is not required Thrive in cross-functional collaboration with both internal teams and external partners Have experience working with external partners to align goals and deliver impact Are a fast learner who can quickly ramp up on new technologies, hardware platforms, and provider ecosystems Are highly autonomous and take ownership of problems end-to-end, including work that falls outside your job description Preferred qualifications Direct experience working with CSPs to scale infrastructure or products across multiple platforms, navigating differences in networking, security, privacy, billing, and managed service offerings Hands-on experience with capacity management, cost optimization, or resource planning at scale across heterogeneous environments Solid understanding of multi-region deployments, geographic routing, and global traffic management Proficiency in Python or Rust The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: San Francisco, CA | Seattle, WA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role The Cloud Inference team scales and optimizes Claude to serve the massive audiences of developers and enterprise companies across AWS, GCP, Azure, and future cloud service providers (CSPs). We own the end-to-end product of Claude on each cloud platform, from API integration and intelligent request routing to inference execution, capacity management, and day-to-day operations. Within Cloud Inference, the model & inference launch team owns the validation pipeline for our inference server and load balancer on these platforms. We're responsible for every inference change — model launches, performance improvements, safeguard integrations — landing on cloud platforms with correctness, performance, and reliability intact. This is high-leverage infrastructure work: validation has to be fast and cheap enough to run on the same accelerators that serve customers, trustworthy enough to replace manual checks, and consistent enough that a change working on Anthropic first-party means it works everywhere. This directly determines how fast frontier models and features ship to every cloud platform, and how quickly performance wins reach production — reclaiming capacity at a time when compute is our scarcest resource. Key responsibilities Be on the critical path for frontier model launches, bringing up inference for new model architectures and shipping them to cloud platforms in lockstep with our first-party platform Work with the core inference team to bring new inference features (e.g. structured sampling, prompt caching, and more) to cloud platforms, owning the platform-specific integration that gets them to production Identify and dive deep on the gaps that make inference behave differently across first-party and CSPs — config drift, observability, deployment patterns, hard cross-platform bugs — and fix them at the source rather than building platform-specific workarounds Design, build, and own the CI/CD infrastructure for the inference server and load balancer across cloud platforms, with shadow traffic, performance baselines (throughput and latency), and correctness checks that catch regressions before production Drive down merge-to-production cycle time by making validation faster, more parallel, and cost-effective enough to run on the same constrained accelerator pool that serves customers, without trading away reliability Analyze observability data across providers to identify performance bottlenecks, cost anomalies, and regressions, and drive remediation based on real-world production workloads Minimum qualifications Have a strong interest in LLM serving; prior inference or ML experience is not required Have significant software engineering experience, with a strong background in high-performance, large-scale distributed systems serving millions of users Have a track record of building automation or test infrastructure that measurably improved release velocity or reliability Have experience building or operating services on at least one major cloud platform (AWS, GCP, or Azure), with exposure to Kubernetes, Infrastructure as Code, or container orchestration Thrive in cross-functional collaboration with both internal teams and external partners Are a fast learner who can quickly ramp up on new technologies, hardware platforms, and provider ecosystems Are highly autonomous and take ownership of problems end-to-end, including work that falls outside your job description Preferred qualifications LLM inference optimization, batching, and caching strategies Capacity-constrained scheduling or shared-resource test infrastructure Solid understanding of multi-region deployments, request routing, load balancing, global traffic management Working with CSP partner teams to scale infrastructure across multiple platforms, navigating differences in networking, security, privacy, and managed service Proficiency in Python or Rust The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: New York City, NY; San Francisco, CA; Seattle, WA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role Our Inference team is responsible for building and scaling the critical systems that serve Claude to millions of users worldwide. We bring Claude to life by serving our models via the industry’s largest compute-agnostic inference deployments. We are responsible for the entire stack from intelligent request routing to fleet-wide orchestration across diverse AI accelerators. The team has a dual mandate: maximizing compute efficiency to reliably serve our explosive customer growth, while enabling breakthrough research by giving our scientists the high-performance inference infrastructure they need to develop next-generation models. We tackle complex, distributed systems challenges across multiple accelerator families and emerging AI hardware running in multiple cloud platforms. Inference systems are highly performance sensitive distributed systems. Inference serves hundreds of thousands of customers every day, and the size & span of the inference fleet requires sophisticated routing, scaling, and networking systems. Key responsibilities Design, build, and maintain the distributed systems that serve Claude to millions of users worldwide Develop resilient, flexible systems that adapt in real time to real world events Develop intelligent request routing, load balancing, and traffic management systems across thousands of accelerators and multiple cloud providers Maximize compute efficiency and optimize cost across the fleet by autoscaling and orchestrating production, research, and experimental workloads across multiple cloud providers Build and operate production-grade deployment pipelines for releasing new models to users Provide high-performance inference infrastructure that enables researchers to develop next-generation models Integrate new AI accelerator platforms and support inference for new model architectures Minimum qualifications Significant software engineering experience, particularly with distributed systems Results-oriented, with a bias towards flexibility and impact Willingness to pick up slack, even if it goes outside your job description Desire to learn more about machine learning systems and infrastructure Thrive in environments where technical excellence directly drives both business results and research breakthroughs Care about the societal impacts of your work Preferred qualifications Experience with high-performance, large-scale distributed systems Experience implementing and deploying machine learning systems at scale Experience with load balancing, request routing, or traffic management systems Familiarity with LLM inference optimization, batching, and caching strategies Experience with Kubernetes and cloud infrastructure (AWS, GCP, Azure) Proficiency in Python or Rust Representative projects Designing intelligent routing algorithms that optimize request distribution across many accelerators in different environments Autoscaling our compute fleet to dynamically match supply with demand across production, research, and experimental workloads Building production-grade deployment pipelines for releasing new models to millions of users reliably Contributing to new inference features Supporting inference for new model architectures Analyzing observability data to tune performance based on real-world production workloads Managing multi-region deployments and geographic routing for global customers The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: Washington DC WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW Harvey is bringing its AI platform to the U.S. federal government — defense and civilian agencies whose legal, acquisition, and program teams are asking how to adopt AI responsibly. You will be Harvey's first Solutions Architect dedicated to public sector: the technical lead on federal opportunities, the person who designs how Harvey is deployed securely inside government environments, and the one who writes the playbook the rest of the team will follow. You will sit at the intersection of Harvey's platform, agency stakeholders (legal, IT, security, acquisition, and program offices), and our Security & Trust, Legal Engineering, Product, and Customer Success teams. This is an opportunity to help define how AI is adopted across the federal government and to build a flagship public-sector practice at one of the fastest-moving companies in applied AI. WHAT YOU'LL DO - Lead technical discovery and solution design for federal prospects and customers — defense and civilian — working with legal, IT, security, acquisition, and program stakeholders. - Design, document, and defend secure, compliant architectures for Harvey in government environments, including Harvey's FedRAMP High environment, identity and access patterns, integrations, and data-handling requirements (CUI, records retention, auditability). - Own the technical thread on a focused portfolio of high-impact federal accounts, from first conversation through rollout, adoption, and expansion. - Be the field-facing technical partner to Security & Trust on agency security reviews — FedRAMP High controls, DoD Impact Levels, ATO and authorization boundaries, NIST 800-53 — and translate them for program owners and counsel. - Work with Legal Engineers and Customer Success Managers to turn agency workflows and legal or policy use cases into concrete solutions, reusable patterns, and implementation playbooks. - Be the public-sector technical voice back into Harvey, synthesizing field feedback to inform roadmap, evaluation criteria, deployment options, and government-specific documentation. - Represent Harvey's technical perspective in public-sector forums — customer workshops, working groups, and stakeholder briefings — helping agencies understand both the possibilities and the limits of current AI systems. - Build the public-sector Solutions Architecture playbook — qualification, security review, deployment patterns, and enablement — so the practice can scale beyond one person. WHAT YOU HAVE - 7+ years in solutions architecture, sales engineering, forward-deployed engineering, or a similar customer-facing technical role. - Direct experience selling to or supporting U.S. federal customers (Department of Defense and/or civilian agencies) at a SaaS or cloud provider, including navigating agency security reviews and procurement. - Working knowledge of federal compliance frameworks and how they shape deployment — FedRAMP, NIST 800-53, DoD Impact Levels, ATO processes — and the ability to explain them to CISOs and program managers alike. - Hands-on depth in SaaS security and identity architecture: SSO/SAML, SCIM, encryption and key management, data residency, audit logging. - Clear communication with technical and non-technical audiences — from program managers and attorneys to CISOs and enterprise architects — and credibility with senior public-sector stakeholders. - Hands-on engagement with modern AI systems (LLMs, prompt design, evaluation, deployment patterns) and a desire to help agencies adopt them responsibly. - Comfort operating in an emerging practice area: energized by ambiguity, willing to build new playbooks, motivated by real-world impact on public services. PREFERRED - Prior government service or military experience. - Experience with Azure Government or other government cloud environments. - Experience selling through federal systems integrators or contract vehicles. - Background in legal, e-discovery, or other document-heavy enterprise workflows. COMPENSATION $218,800 - $300,900 USD OTE with a 80/20 split LOCATION Remote within Washington, DC area - travel approximately 25–40% to agency and partner sites. DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-TM1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai

Location: San Francisco, California; New York, New York Workplace: Hybrid WHO WE ARE Notion is the collaborative AI workspace where teams and agents think together https://www.youtube.com/watch?v=vkpYpWfEK5s. We're building one place where your knowledge, projects, meetings, and AI tools live side by side, so work is faster, clearer, and less fragmented. Millions of individuals, small teams, and large companies run their work on Notion. Notinos (our employees) are customer zero in bringing this future of work to life. We care about craft, building things that last, and the belief that great work is still fundamentally human. Our goal isn’t to ship the next feature. Each and every team of Notinos is working to set the standard for how humans work together in the AI era. From building a business’s system of record to making and managing AI agents to automating away the busy work, we care deeply about giving our customers more time for their life’s work. ABOUT THE ROLE Millions of people rely on Notion to do their most important work, and protecting that trust is foundational to everything we build. We’re looking for a hands-on Detection Engineer to build and operate the systems and workflows we use to detect and respond to attacks across Notion’s cloud-native environment. You’ll ship high-signal detections, improve the platform that powers them, participate in incident response, and help shape how detection and response engineering scales at Notion. You’ll work closely with Engineering, Corporate Security, and Infrastructure, with broad latitude to identify gaps, prioritize investments, and build what’s needed next. We view detection and response as a software engineering discipline: detections are code, platforms are products, and measurement matters WHAT YOU'LL ACHIEVE - Build and tune high-signal detections across cloud, identity, endpoint, and SaaS environments, with review and mentorship from senior teammates as you ramp up. - Contribute to the detection platform, including rule lifecycle management, tuning, measurement, and rollout safety. - Build tooling and automation that speed up triage, enrichment, investigation, and detection authoring, including LLM-based workflows where useful. - Turn threat intelligence and adversary TTPs into detections, telemetry requirements, and response improvements. - Take part in investigations, incident response, and postmortems, and help turn what we learn into lasting fixes. - Help define and track metrics such as coverage, MTTD, and alert quality. - Join a shared on-call rotation for incident response. SKILLS YOU'LL NEED TO BRING - We're hiring across a range of experience levels. If you have some of these skills but not all, we'd still like to hear from you. - 3+ years of experience in detection engineering, security operations, incident response, threat hunting, or a closely related security or software engineering role. - Have written or tuned detections that run in production, and care about signal quality and cutting noise. - Working knowledge of at least one detection or query language (Sigma, KQL, SPL, YARA-L, EQL, or Panther), or strong SQL or Python skills and the drive to learn one quickly. - An understanding of how attackers operate (for example, MITRE ATT&CK), and the ability to use it to decide what to detect. - Hands-on experience with AWS, GCP, or Azure, ideally including identity and access logs. - Have used SIEM, EDR, or SOAR tools in any size of environment. - Write clearly in runbooks, design docs, and incident notes, and can own well-scoped projects end to end. NICE TO HAVE - Have led purple team, blue team, or adversary emulation exercises that improved detections or telemetry. - Experience running SIEM, EDR, or SOAR platforms at large scale. - Experience building or maintaining detection-as-code workflows (version control, CI, and tests for detections). - Experience applying LLMs or agent-style tooling to security workflows. - Experience securing AI-enabled systems or endpoint tooling. - Kubernetes or container detection experience. - Background in threat intelligence, malware analysis, or digital forensics. - Contributions to the security community through research, tooling, CTFs, or talks. - Experience at a high-growth startup or AI company. Notion is committed to providing highly competitive cash compensation, equity, and benefits. The compensation offered for this role will be based on multiple factors such as location, the role’s scope and complexity, and the candidate’s experience and expertise, and may vary from the range provided below. For roles based in San Francisco or New York City, the estimated base salary range for this role is $185,000 - $280,000 per year.   By clicking “Submit Application”, I understand and agree that Notion and its affiliates and subsidiaries will collect and process my information in accordance with Notion’s Global Recruiting Privacy Policy https://notion.notion.site/Notion-Global-Recruiting-Privacy-Policy-fc3eb4e829354a26a2bb6fd5e289b550 and and NYLL 144 https://notion.notion.site/Ashby-AI-Bias-Audit-2b0efdeead05803bbbfae159ec86c528.   #LI-Onsite A NOTE ON AI You don’t need deep AI expertise for every role, but we do expect every Notino to be intellectually curious, drawn to tinkering and discovery, and excited to use AI as a real collaborator in their work. For some roles, AI fluency is a core requirement — when that’s the case, we'll say so explicitly in the qualifications. People who thrive here don’t treat AI as a novelty. They use it to think better, and make their work easier for others to build on. EQUAL OPPORTUNITY & ACCOMMODATIONS We hire talented people from a wide range of backgrounds. If you’re excited about this role but don’t meet every bullet, we still encourage you to apply. Notion is an equal opportunity employer and does not discriminate on the basis of any legally protected characteristic. Consistent with applicable law, we will consider for employment qualified applicants with arrest and conviction records. Notion provides reasonable accommodations during the application process; if you need one, please let your recruiter know. Notion is proud to be an equal opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristic. Notion considers qualified applicants with criminal histories, consistent with applicable federal, state and local law. Notion is also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, please let your recruiter know.

Location: Dublin, Ireland Workplace: Hybrid WHO WE ARE Notion is the collaborative AI workspace where teams and agents think together https://www.youtube.com/watch?v=vkpYpWfEK5s. We're building one place where your knowledge, projects, meetings, and AI tools live side by side, so work is faster, clearer, and less fragmented. Millions of individuals, small teams, and large companies run their work on Notion. Notinos (our employees) are customer zero in bringing this future of work to life. We care about craft, building things that last, and the belief that great work is still fundamentally human. Our goal isn’t to ship the next feature. Each and every team of Notinos is working to set the standard for how humans work together in the AI era. From building a business’s system of record to making and managing AI agents to automating away the busy work, we care deeply about giving our customers more time for their life’s work. ABOUT THE ROLE Millions of people rely on Notion to do their most important work, and protecting that trust is foundational to everything we build. We’re looking for a hands-on Detection Engineer to build and operate the systems and workflows we use to detect and respond to attacks across Notion’s cloud-native environment. You’ll ship high-signal detections, improve the platform that powers them, participate in incident response, and help shape how detection and response engineering scales at Notion. You’ll work closely with Engineering, Corporate Security, and Infrastructure, with broad latitude to identify gaps, prioritize investments, and build what’s needed next. We view detection and response as a software engineering discipline: detections are code, platforms are products, and measurement matters WHAT YOU'LL ACHIEVE - Build and tune high-signal detections across cloud, identity, endpoint, and SaaS environments, with review and mentorship from senior teammates as you ramp up. - Contribute to the detection platform, including rule lifecycle management, tuning, measurement, and rollout safety. - Build tooling and automation that speed up triage, enrichment, investigation, and detection authoring, including LLM-based workflows where useful. - Turn threat intelligence and adversary TTPs into detections, telemetry requirements, and response improvements. - Take part in investigations, incident response, and postmortems, and help turn what we learn into lasting fixes. - Help define and track metrics such as coverage, MTTD, and alert quality. - Join a shared on-call rotation for incident response. SKILLS YOU'LL NEED TO BRING - We're hiring across a range of experience levels. If you have some of these skills but not all, we'd still like to hear from you. - 3+ years of experience in detection engineering, security operations, incident response, threat hunting, or a closely related security or software engineering role. - Have written or tuned detections that run in production, and care about signal quality and cutting noise. - Working knowledge of at least one detection or query language (Sigma, KQL, SPL, YARA-L, EQL, or Panther), or strong SQL or Python skills and the drive to learn one quickly. - An understanding of how attackers operate (for example, MITRE ATT&CK), and the ability to use it to decide what to detect. - Hands-on experience with AWS, GCP, or Azure, ideally including identity and access logs. - Have used SIEM, EDR, or SOAR tools in any size of environment. - Write clearly in runbooks, design docs, and incident notes, and can own well-scoped projects end to end. NICE TO HAVE - Have led purple team, blue team, or adversary emulation exercises that improved detections or telemetry. - Experience running SIEM, EDR, or SOAR platforms at large scale. - Experience building or maintaining detection-as-code workflows (version control, CI, and tests for detections). - Experience applying LLMs or agent-style tooling to security workflows. - Experience securing AI-enabled systems or endpoint tooling. - Kubernetes or container detection experience. - Background in threat intelligence, malware analysis, or digital forensics. - Contributions to the security community through research, tooling, CTFs, or talks. - Experience at a high-growth startup or AI company. Notion is committed to providing highly competitive cash compensation, equity, and benefits. The compensation offered for this role will be based on multiple factors such as location, the role's scope and complexity, and the candidate's experience and expertise, and may vary from the range provided below. The estimated base salary range for this role is €75,000–€142,000 per year. By clicking “Submit Application”, I understand and agree that Notion and its affiliates and subsidiaries will collect and process my information in accordance with Notion’s Global Recruiting Privacy Policy https://notion.notion.site/Notion-Global-Recruiting-Privacy-Policy-fc3eb4e829354a26a2bb6fd5e289b550?pvs=74. #LI-Onsite A NOTE ON AI You don’t need deep AI expertise for every role, but we do expect every Notino to be intellectually curious, drawn to tinkering and discovery, and excited to use AI as a real collaborator in their work. For some roles, AI fluency is a core requirement — when that’s the case, we'll say so explicitly in the qualifications. People who thrive here don’t treat AI as a novelty. They use it to think better, and make their work easier for others to build on. EQUAL OPPORTUNITY & ACCOMMODATIONS We hire talented people from a wide range of backgrounds. If you’re excited about this role but don’t meet every bullet, we still encourage you to apply. Notion is an equal opportunity employer and does not discriminate on the basis of any legally protected characteristic. Consistent with applicable law, we will consider for employment qualified applicants with arrest and conviction records. Notion provides reasonable accommodations during the application process; if you need one, please let your recruiter know. Notion is proud to be an equal opportunity employer. We do not discriminate in hiring or any employment decision based on race, color, religion, national origin, age, sex (including pregnancy, childbirth, or related medical conditions), marital status, ancestry, physical or mental disability, genetic information, veteran status, gender identity or expression, sexual orientation, or other applicable legally protected characteristic. Notion considers qualified applicants with criminal histories, consistent with applicable federal, state and local law. Notion is also committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If you need assistance or an accommodation due to a disability, please let your recruiter know.

В компании Azur Games открыта вакансия Middle QA Engineer для проекта Railroad Empire: Train Game. В обязанности входит проведение тестирования игр, выявление и документирование багов, а также взаимодействие с командой разработчиков для улучшения качества продукта. Требуется опыт работы в QA, знание методологий тестирования, умение работать с баг-трекинговыми системами и понимание процессов разработки игр. Возможна удалённая работа или работа в офисе. Кандидат должен быть внимателен к деталям, ответственен и уметь эффективно коммуницировать в команде.

Location: Bulgaria, Hungary, Poland Why Join Exadel We’re an AI-first global tech company with 25+ years of engineering leadership, 2,000+ team members, and 500+ active projects powering Fortune 500 clients, including HBO, Microsoft, Google, and Starbucks. From AI platforms to digital transformation, we partner with enterprise leaders to build what’s next. What powers it all? Our people are ambitious, collaborative, and constantly evolving. About the Client The leading provider of vehicle lifecycle solutions, with headquarters in Chicago, enables the companies that build, insure, and replace vehicles to power the next generation of transportation. Its platform delivers advanced mobile, artificial intelligence, and car technologies. It connects a network of 350+ insurance companies, 24,000+ repair facilities, hundreds of parts suppliers, and dozens of third-party data and service providers. The customer's collective solutions enhance productivity and help clients deliver better experiences for end consumers. What You’ll Do Design and implement end-to-end document intelligence pipelines on AWS Develop and optimize ML models for document classification, segmentation, and field extraction Build scalable data processing systems handling PDFs up to 2000 pages Collaborate with subject matter experts to create and refine requirements for extraction Own features from research through production deployment and monitoring Establish evaluation frameworks and quality metrics for extraction accuracy What You Bring Advanced knowledge of Python (native, Pandas, ScikitLearn, Tensorflow or Pytorch, PyStats, Pydantic) Experience with AWS tools for ML Engineering and ML deployment (Sagemaker, Lambda, Cloudformation/CDK, Step Functions) Advanced knowledge of SQL and Data Modeling Experience with GenAI for document intelligence, including prompt engineering, RAG (Retrieval Augmented Generation), multi-modal models (vision + text), and production deployment using AWS Bedrock or Azure OpenAI APIs Experience in experiment design (power analysis and hypothesis testing) Proficiency in both written and verbal communication, required for a remote and largely asynchronous work environment Demonstrated capacity to clearly and concisely communicate complex technical problems and propose iterative solutions Experience owning a feature from concept to production, including proposal, discussion, and execution Nice to have Experience with document processing tools (AWS Textract, Azure Document Intelligence, or similar OCR/layout detection systems) Experience with PDF and Image processing libraries (e.g. PyMuPDF, opnecv, pillow) Experience in Machine Learning/ Data Science (e.g., ML algorithm selection, feature engineering, model training, hyperparameter tuning, supervised and unsupervised learning implementation, building a model pipelines, using Machine Learning tools/libraries/frameworks) Experience working with AWS big data technologies (Redshift, S3, EMR, Glue, etc.) English Level Upper-intermediate Legal & Hiring Information Exadel is proud to be an Equal Opportunity Employer committed to inclusion across minority, gender identity, sexual orientation, disability, age, and more. Reasonable accommodations are available to enable individuals with disabilities to perform essential functions. Please note: this job description is not exhaustive. Duties and responsibilities may evolve based on business needs. Compensation details are shared with candidates at the early stage of the recruitment process. The offer is not binding until a signed contract is in place. Your Benefits at Exadel Exadel benefits vary by location and contract type. Your recruiter will fill you in on the details. International projects In-office, hybrid, or remote flexibility Medical healthcare Recognition program Ongoing learning & reimbursement Well-being program Team events & local benefits Sports compensation Referral bonuses Top-tier equipment provision Exadel Culture We lead with trust, respect, and purpose. We believe in open dialogue, creative freedom, and mentorship that helps you grow, lead, and make a real difference. Ours is a culture where ideas are challenged, voices are heard, and your impact matters.

Location: Remote - India AI/ML Solution Architect Education & Assessment Platforms About Pratham International Pratham International is a US 501(c)(3) established to support learning innovations for children and youth in contexts across the globe. We enable the adaptation and spread of proven and scalable education solutions pioneered in India by Pratham Education Foundation to contexts across the global south through programming and technical assistance tailored to local realities. Today, Pratham innovations reach millions of children annually through partnerships across ~30 countries. Through the Pratham Shah PraDigi Innovation Centre (PraDigi), Pratham International also seeks to create technology innovations for low-resource communities, and support models aimed at learning for school, work, and life for children and youth. We work on several cutting-edge projects in the domain of Generative AI, Automatic Speech Recognition, and Natural Language Processing. The team’s innovative technology products include the PadhAI: Reading Assessment App, which uses advanced speech recognition to evaluate children’s reading fluency in 8 Indian languages and 6 global languages; Anytime Testing Machine (ATM), an AI-enabled question generation and grading system that allows learners to be assessed on any topic, anytime; and a suite of AI-powered chatbots designed for diverse learning contexts: from early childhood care, to youth employment readiness, and OCR-based analysis tools that digitize and assess student work at scale. At Pratham International, we build for the long term. Whether it’s ensuring products are truly accessible across rural India and the global south through languages, building datasets to ensure context and accuracy for products, or gathering feedback from the field. Backed by partners like Google.org, Anthropic, and the Gates Foundation, we are committed to building digital public goods that ensure every child is in school and learning well. Role Overview About the work We design and deliver AI systems that governments and large education programmes can actually run: curriculum-aligned item generation, paper assembly, handwriting OCR, rubric grading, and teacher-in-the-loop review. The work is already live as practice assessment in India and is being built as a national exam platform in Rwanda, with further country programmes behind it. We are hiring a Solution Architect who sits between discovery and delivery. You will turn messy workshop notes and ministry requirements into architecture a team can build, secure, cost, and operate — RAG and multi-agent systems, LLM-as-judge pipelines, and the cloud/LLMOps layer underneath. You will Own solution design from first workshop to a delivery-ready blueprint (services, data stores, trust boundaries, cost, ops). Architect LLM pipelines: generation, retrieval over curriculum knowledge graphs, standards/duplication/quality judges, constraint-based paper assembly. Design human + AI workflows with audit, segregation of duties, cycle-pinned config, and no student PII in model calls. Choose and govern models, prompts, eval gates, and fallbacks (including OCR: Azure primary, secondary fallback). Shape the Azure/AWS platform: identity, networking, LLMOps/MLOps, observability, and sovereign-hosting constraints. Run client workshops, write POVs and architecture packs, and stay accountable until the system is in production. About the work Pratham International programmes sit at the intersection of pedagogy, public systems, and production AI. The pattern is consistent across countries; the constraints change. Anytime Testing Machine (ATM) — India, practice and feedback End-to-end assessment for real classroom conditions: generate curriculum-aligned items, digitise handwritten answers (often photographed on a phone), grade against rubrics, and return feedback a teacher reviews before it reaches the learner. Built for high pupil–teacher ratios, mixed language (e.g. Hindi + English), and uneven connectivity. Pilots have already reached thousands of learners, including Second Chance (women returning for Grade 10). Model quality is measured against expert golden sets, not vendor slides. National assessment automation A government-grade exam platform. Phase 1 is item authoring and print-ready paper composition before the exam window. Phase 2 is script intake, OCR at national volume, AI-assisted grading with human confirmation on the pilot, results export, and monitoring/evaluation reporting. Subjects span core and elective streams across the curriculum. Design rules that do not exist in a typical SaaS product: cycle-pinned configuration, embargoed papers, hash-chained audit trails, and integration with national student information and assessment management systems. Curriculum knowledge graphs Structured retrieval corpora (concepts + worked examples) that ground generation and judging. This is the standing public-knowledge exception in an otherwise closed exam system, and the seed of a broader education DPI conversation. AI in TaRL and teacher support AI tools that help teachers group and teach by learning level, with an RCT-shaped evidence bar. Same product family: human agency stays in the loop; the model is a capacity multiplier. Country programmes and the three-year direction Adapt the same engine to new ministries, languages, and exam rules (Kenya and other Global South programmes are in scope). The longer bet is a learning and credentialing engine that recognises competence beyond a single syllabus. This is not a chatbot brief. It is constrained generation, retrieval, judging, document AI, workflow, and public-sector operations. Why this role exists Most enterprise AI dies between the workshop and production. We need one person who owns that gap: someone who can sit with NESA, C4IR, Pratham pedagogy, and engineering in the same week, and leave behind a blueprint the delivery team can execute without inventing policy. The target profile is a practising AI/ML Solution Architect — not a pre-sales slide owner, and not an infra-only platform engineer. The person should have done all three of these: Designed GenAI / NLP solutions, PoCs, and client workshops, and can talk to research-grade issues (eval, agent safety, prompt-injection, model-specific behaviour). Owned the AI platform: LLMOps, MLOps, DevSecOps, SRE habits, cloud migration, and FinOps. Taken messy discovery and turned it into Azure-centred RAG and multi-agent designs that a security team will accept. Key Responsibilities Discovery → architecture Lead solution workshops with programme, ministry, and engineering stakeholders. Convert half-formed requirements into service cuts, data stores, sequence diagrams, decision gates, and open-question lists with owners. Write architecture decision records. When the docs disagree (they will), force a decision instead of coding both. AI system design Design LLM jobs as contracts: inputs, JSON schema, temperature, persist prompt hash / model / tokens / latency. Architect generation grounded in structured curriculum blueprints and knowledge-graph retrieval, not "ask the model for a maths paper." Design LLM-as-judge banks (cognitive-level, relevance, difficulty, language, fairness, standards, duplication, blueprint compliance) with thresholds, one-repair rules, and golden-dataset release gates. Design hybrid assembly: constraint solver first, model only on ties and layout exceptions. Design document AI: primary OCR provider, secondary fallback provider, confidence routing, de-identification before any grading call. Specify eval: inter-rater agreement, rubric-band exact match, first-pass item acceptance, cost per paper cycle. Platform, trust, cost Own the reference architecture on the chosen cloud platform(s): identity, RBAC + segregation of duties, private networking, key management, object storage for scans, vector index, data warehouse for monitoring/evaluation reporting. Draw trust boundaries. Student PII never crosses the model gateway. Prompts are curriculum + knowledge-graph + blueprint content only. Put FinOps on the design: token budgets, OCR cost caps, degradation behaviour when a cap is hit, model routing. Specify LLMOps/MLOps: prompt/version registry, eval regression as a release gate, observability, rollback. Design for sovereign hosting and exam-cycle operations (embargo, watermarking, append-only audit, cycle-pinned configuration). Delivery and thought leadership Stay with the build through the first production cycle. Architecture that cannot be implemented is unfinished work. Produce POVs, sequence packs, and workshop artefacts that can be reused in the next country. Mentor tech leads on where the model should stop and the workflow should start. Success in the first two quarters First 30 days — Orient Complete architecture walkthroughs of every live/in-flight system with the current engineering leads; document current state (services, data stores, trust boundaries) as-is, not as-designed Meet every active stakeholder group once: engineering, programme/domain leads, and any external partner or government-side counterpart Inventory all open technical decisions and disagreements in existing specs; log each with current owner (if any) and status Identify the single highest-risk unresolved design question and flag it upward Day 60 — Diagnose Produce a current-state architecture diagram covering every major pipeline: data flow, model/LLM touchpoints, storage, and identity/access boundaries Assign an owner and target resolution date to every open contradiction identified in month 1 Draft a v1 reference architecture for one representative end-to-end workflow: service breakdown, data stores, identity/access model, model gateway placement, and a first-pass cost estimate Day 90 — Commit v1 reference architecture is reviewed and accepted by engineering and relevant stakeholders (not just self-signed) Can walk any core pipeline end-to-end from memory and name every data store, every quality/eval gate, and every trust boundary in it At least one architecture decision record is published for a previously-contested design question, with the decision forced rather than left open A first cost model exists for the reference architecture, with at least one identified lever for reducing spend if usage scales Day 120 — Harden Core pipeline components (generation, retrieval, judging/eval, or equivalent stages for this domain) have documented contracts: defined inputs/outputs, schema, and logged metadata (model, version, cost, latency) An evaluation harness exists for at least one component, with a defined pass/fail threshold A release gate exists that can block a bad prompt, model, or config change from reaching production Day 150 — Extend The next major system phase or capability is specified to the same rigor as the first: trust boundaries, failure modes, fallback behavior, and cost caps documented before build starts FinOps guardrails are in place for at least one production workload: budget ceilings, degradation behavior when a cap is hit, and a routing or fallback strategy Day 180 — Generalize A reusable design pattern or adaptation pack exists, separating what is fixed in the architecture from what varies by deployment context (e.g. language, hosting environment, external-system integration, content/domain rules) At least one engineering or technical lead has been mentored on where model-based logic should stop and deterministic/workflow logic should take over A second production-readiness review (security, cost, or operations) has been passed on a system this person designed or substantially redesigned Qualifications and Experience Must have 8+ years building software or data/AI systems, with recent experience as a Solution Architect, AI/ML Architect, or Principal/Staff Engineer with end-to-end architecture ownership. Shipped at least one production AI/ML system — RAG, multi-agent, LLM-as-judge, or a comparable classical ML/NLP pipeline at scale — that passed a real security, cost, and operations review, not just a pilot or a slide deck. Can produce a delivery-ready architecture blueprint: services, data stores, IAM, failure modes, evaluation strategy, and cost model — detailed enough that engineering isn't guessing at intent. Deep cloud experience on Azure and/or AWS: comfortable defending a landing-zone design, private-endpoint architecture, and a real monthly compute/inference/OCR spend. Fluent in Python, with hands-on experience in both classical ML (model training, evaluation, feature pipelines) and how LLM APIs, embeddings, and vector search actually behave under production load. Comfortable running a room with non-engineers — domain specialists, IT stakeholders, monitoring/evaluation teams — and translating fluently in both directions. Strong written English; this role's output is documentation that functions as a binding technical contract. Strongly preferred Azure AI (AI Foundry / OpenAI / Document Intelligence), plus one of AWS (Bedrock, SageMaker) or GCP (Vertex, BigQuery). LLMOps / MLOps: prompt + model versioning, MLflow or equivalent, eval harnesses, CI that can block a bad prompt. Document AI / OCR at volume; handwriting and low-quality scans, not only clean PDFs. RAG over structured knowledge (graphs, ToS, textbooks) and hybrid search, not only naive chunking. Education, assessment, public sector, or other high-audit domains (health, finance, identity). Certifications: Azure Data Scientist / Azure Solutions Architect; AWS SAA, ML Specialty, or Data Analytics Specialty. Experience with agent security: tool-use limits, DLP on egress, prompt-injection evaluation that is model-specific. FinOps discipline. You have killed a design because it would not survive the token bill. Nice to have Microsoft Fabric, Databricks, Semantic Kernel / Microsoft Agent Framework, MCP (Model Context Protocol) tool integration for agentic workflows. Constraint solvers or exam-blueprint / item-bank systems for automated test assembly. Multilingual generation and feedback, including code-mixed language pairs (e.g. Hindi–English, Spanish–English, or similar). Experience standing up golden/eval datasets with subject-matter experts. Prior work with government data systems, data-sharing agreements, or in-country/sovereign hosting requirements. What this role is not Not a research-only post. Publications help; shipping is the job. Not a pure platform/SRE hire. Infra is part of the design, not the whole design. Not a prompt engineer who hands off “the architecture” to someone else. Not a people manager of a large team on day one — you influence through artefacts and reviews. How we will assess you We value transparency and respect your time. Our hiring process is designed to evaluate both hands-on technical depth and architectural judgment through practical, real-world tasks. Stage 1: Application & Knockout Screening Submit your CV alongside key screening responses Artefact — send two of: architecture note, eval design, runbook, ADR. Slides without decisions do not count. Stage 2: Initial Conversation (30 mins) An introductory screen covering your background, project context and baseline alignment. An opportunity for you to ask questions about our technical roadmap, partner ecosystem, and vision. Stage 3: Practical Take-Home Written Assessment (2–3 hours) Design exercise on a constrained generation + judge + human-review pipeline (we will use a sanitised exam-authoring brief). We want trust boundaries, eval, and cost, not a model-name dump. Stage 4: Deep-Dive Technical & System Architecture Walkthrough Walkthrough of a system you shipped: what failed in review, what you changed, what it costs to run. Stage 5: Final Stakeholder & Vision Alignment Final conversation with key technical leadership and program stakeholders. Employment Details Function: Solutions / Architecture Reports to: Head of Technology / CTO Expected Start Date: Immediate Location: India (remote-first; travel to client workshops as needed) Experience: 8–13 years (strong 6–8 considered if you have shipped production GenAI) Engagement: Full-time Remuneration: Based on candidate’s experience and skillset Flexibility to work across timezones: Our team spans Latin America to Southeast Asia. Early mornings  or late evenings can be a regular part of the rhythm Why join us? Impact at Scale: Your work will directly support the learning journeys of millions of children. With programs active in 31 countries, you will help build Digital Public Goods that bridge the gap for the millions of children across the Global South who still struggle with foundational literacy Innovation at the intersection of Education and Technology: Lead the charge in redefining learning and learning processes through technology. You will contribute to the design and development of cutting-edge learning tools and AI-driven initiatives, ensuring EdTech isn’t just a buzzword, but a scalable solution for global equity. A Culture of Growth and Evidence: At Pratham, we value rigorous testing and continuous learning. You will have the autonomy to experiment with the latest SOTA models, supported by a 5-year strategic roadmap and funding from partners like the Gates Foundation and Anthropic. High-Calibre, Mission-Driven Team: You will work alongside a global team of experts from institutions like MIT, Harvard, and INSEAD. Our alumni have gone on to lead successful startups and international policy shifts, all while remaining dedicated to the goal of “Every Child in School and Learning Well.” Inclusive and Collaborative Environment: Join a diverse team that spans Asia, Africa, and Latin America. We value innovative thinking and local expertise, ensuring that the technology we build is deeply rooted in the realities of the communities we serve. Pratham International is an equal opportunity employer and encourages all qualified people from diverse backgrounds to apply for positions within our organisation, regardless of race, gender, disability or any other status.

ASOS.com — британский онлайн‑ритейлер модной одежды, обуви, аксессуаров и косметики. Размер команды — 1k-5k, инвестиции — £587.5M. Технологии и инструменты: Microsoft Copilot Studio, Azure AI Foundry, Azure OpenAI, Semantic Kernel, LangChain, LangGraph, AutoGen/CrewAI, Python или C#/.NET. Фаундеры компании: Nick Robertson, Andrew Regan, Quentin Griffiths, Deborah Thorpe. В компании работают русскоговорящие сотрудники.

Location: Canada (Remote) Grafana Labs is the company behind Grafana Cloud, the fully managed observability platform trusted by more than 10,000 organizations to ensure reliability, resolve incidents faster, and optimize telemetry at scale. Built on open source and open standards and designed for interoperability across any stack, Grafana Cloud brings AI to observability and observability to AI, giving teams (and their agents) unified visibility so they can see, understand, and act on all their disparate data, wherever it lives, and move at the speed of their ambitions. Customers, including Anthropic, Bloomberg, NVIDIA, Microsoft, and Salesforce, rely on Grafana Labs. We are a 100% remote company with team members across 40+ countries, backed by leading investors including Lightspeed Venture Partners, Sequoia Capital, GIC, Coatue, J.P. Morgan, CapitalG, and Lead Edge Capital. Learn more at grafana.com and follow us on LinkedIn and X . We’re scaling fast and staying true to what makes us different: an open-source legacy, a global collaborative culture, and a passion for meaningful work. Our team thrives in an innovation-driven environment where transparency, autonomy, and trust fuel everything we do. You may not meet every requirement, and that’s okay. If this role excites you, we’d love you to raise your hand for what could be a truly career-defining opportunity. This is a remote opportunity, and we would be interested in applicants located in the US or Canada. The Opportunity: At Grafana Labs, we build observability tools that help users understand, respond to, and improve their systems – regardless of scale, complexity, or tech stack. We recently started a skunkworks initiative with a mission to bring observability to the rest of the business. Our goal is to make Grafana the single best place where humans and AI agents understand and act on real-time data from across the enterprise. As part of this initiative, we are building an AI-native data intelligence system that gives agents reliable, governed access to enterprise context. This system helps agents retrieve the right data, metadata, definitions, lineage, quality signals, and institutional knowledge without every agent builder having to maintain their own brittle context files. The principle is simple: the context system retrieves, agents decide, and data teams maintain the intelligence. The new team is a mix of seasoned Grafanistas and new hires. We operate with a high degree of autonomy and ownership, both as individuals and as a team. Engineers are empowered to make decisions, move quickly, and validate ideas early – while being supported by a deeply collaborative culture that values curiosity, feedback, and cross-functional partnership. We’re looking for a Senior Software (Database) Engineer to help build the underlying storage system for our general purpose data platform. The database layer has both OLAP and OTLP capabilities to support classic analytics queries while also serving up freshly stored data. It embraces the separation of compute and storage and is designed to run on multiple cloud providers. This is an early-stage role on a high-autonomy team, so you should be comfortable working through ambiguity, making pragmatic architectural decisions, and building systems that can evolve from internal dogfooding to production-grade SaaS. As the team matures, there’s a broad opportunity to expand or redefine this role based on impact and initiative. What You'll Be Doing: Take ownership over and build one or multiple parts of the database: Design, implement, test, and operate ingestion, query planning, distributed query execution, data formats, storage formats. Create a scalable SaaS foundation: Help define and build the architecture for a multi-tenant database service, including tenant isolation, usage tracking, quotas, audit logs, background jobs, and reliable service boundaries. Power agent-facing retrieval workflows: Build APIs and database interfaces that allow AI agents, MCP tools, CLIs, and internal applications to retrieve data quickly. Work across product and infrastructure: Partner with the team to make practical tradeoffs between fast experimentation and long-term reliability, especially as the project moves from prototype to production. Operate what you build: Instrument database services with metrics, logs, traces, alerts, and dashboards. Use observability tools to understand system behavior and improve reliability. Contribute to technical direction: Help shape the architecture, service boundaries, storage choices, API contracts, deployment patterns, and engineering practices for a new product area. Effective communication: You’ll be working in a highly dynamic and collaborative environment, so we need someone who can communicate effectively and contribute across teams. Ownership and impact: Take full ownership of the database solutions you develop, ensuring they are not only innovative but also scalable, maintainable, and aligned with real user workflows. What Makes You a Great Fit: Strong engineering skills: Solid experience building production-grade, user-facing software systems. You’re a self-starter, capable of tackling complex engineering problems and making design decisions with minimal supervision. AI experience with a practical mindset: You’re familiar with AI technologies and frameworks, and you focus on delivering high-quality solutions that work in the real world, not just in theory. Quick iteration and experimentation: You’re comfortable releasing prototypes, collecting feedback, and iterating with a pragmatic mindset. Proven initiative: You take ownership and drive projects forward, pushing boundaries to find the most impactful solutions. You can deal with ambiguity and are able to define scope where things are loosely defined. Collaborative attitude: You communicate effectively with your peers. You’re open to feedback, and you bring a solutions-oriented mindset to the table. Experience with distributed systems, catalogs and table formats, query engines. Mastery of a programming language like golang, C++, or Rust Proven track record of delivering software that made it into production and is actively used by users. Exposure to working in cloud-native environments (e.g., AWS, GCP, Azure). Experience using observability tools to understand and troubleshoot system behavior. Bonus Points for: Experience building distributed query engines. Experience building data warehouses and/or data lakes. Experience building tools for data engineering. How we work: We are a remote-first team that meets regularly over video and does most of our work asynchronously, in writing. We value creativity, diverse perspectives, and clear communication. Tempo is relied upon by prominent global organizations to monitor critical applications and infrastructure, and we expect everyone on the team, including our Staff engineers, to contribute ideas that make it a more reliable, more useful, and more loved product. Compensation & Rewards: In Canada, the Base compensation range for this role is $ 164,490 - $197,389. Actual compensation may vary based on level, experience, and skillset as assessed throughout the interview process. All of our roles include Restricted Stock Units (RSUs), giving every team member ownership in Grafana Labs' success. We believe in shared outcomes—RSUs help us stay aligned and invested as we scale globally. *Compensation ranges are country specific. If you are applying for this role from a different location than listed above, your recruiter will discuss your specific market’s defined pay range & benefits at the beginning of the process. Why You’ll Thrive at Grafana Labs: 100% Remote, Global Culture - As a remote-only company, we bring together talent from around the world, united by a culture of collaboration and shared purpose. Scaling Organization – Tackle meaningful work in a high-growth, ever-evolving environment. Transparent Communication – Expect open decision-making and regular company-wide updates. Innovation-Driven – Autonomy and support to ship great work and try new things. Open Source Roots – Built on community-driven values that shape how we work. Empowered Teams – High trust, low ego culture that values outcomes over optics. Career Growth Pathways – Defined opportunities to grow and develop your career. Approachable Leadership – Transparent execs who are involved, visible, and human. Passionate People – Join a team of smart, supportive folks who care deeply about what they do. In-Person onboarding - We want you to thrive from day 1 with your fellow new ‘Grafanistas’ to learn all about what we do and how we do it. Balance is Key - We operate a global annual leave policy of 30 days per annum. 3 days of your annual leave entitlement are reserved for Grafana Shutdown Days to allow the team to really disconnect. *We will comply with local legislation where applicable. Equal Opportunity Employer: Grafana Labs is an equal opportunities employer. We welcome applications from everyone regardless of race, colour, nationality, origin, caste, sex, gender reassignment identity or expression, sexual orientation, age, religion or belief, disability, veteran status, genetic information, pregnancy, maternity, marital, family or carer status, or any other characteristic which is protected by local law. We believe that equality and diversity build a strong organisation, and we work hard to ensure that is the foundation of our organisation as we grow. Grafana Labs may utilize AI tools in its recruitment process to assist in matching information provided in CVs to job postings. The recruitment team will continue to review inbound CVs manually to identify alignment with current openings. #LI-Remote For information about how your personal data is used once you’ve applied to a job, check out our privacy policy .

Location: United States (Remote) Grafana Labs is the company behind Grafana Cloud, the fully managed observability platform trusted by more than 10,000 organizations to ensure reliability, resolve incidents faster, and optimize telemetry at scale. Built on open source and open standards and designed for interoperability across any stack, Grafana Cloud brings AI to observability and observability to AI, giving teams (and their agents) unified visibility so they can see, understand, and act on all their disparate data, wherever it lives, and move at the speed of their ambitions. Customers, including Anthropic, Bloomberg, NVIDIA, Microsoft, and Salesforce, rely on Grafana Labs. We are a 100% remote company with team members across 40+ countries, backed by leading investors including Lightspeed Venture Partners, Sequoia Capital, GIC, Coatue, J.P. Morgan, CapitalG, and Lead Edge Capital. Learn more at grafana.com and follow us on LinkedIn and X . We’re scaling fast and staying true to what makes us different: an open-source legacy, a global collaborative culture, and a passion for meaningful work. Our team thrives in an innovation-driven environment where transparency, autonomy, and trust fuel everything we do. You may not meet every requirement, and that’s okay. If this role excites you, we’d love you to raise your hand for what could be a truly career-defining opportunity. This is a remote opportunity, and we would be interested in applicants located in the US or Canada. The Opportunity: At Grafana Labs, we build observability tools that help users understand, respond to, and improve their systems – regardless of scale, complexity, or tech stack. We recently started a skunkworks initiative with a mission to bring observability to the rest of the business. Our goal is to make Grafana the single best place where humans and AI agents understand and act on real-time data from across the enterprise. As part of this initiative, we are building an AI-native data intelligence system that gives agents reliable, governed access to enterprise context. This system helps agents retrieve the right data, metadata, definitions, lineage, quality signals, and institutional knowledge without every agent builder having to maintain their own brittle context files. The principle is simple: the context system retrieves, agents decide, and data teams maintain the intelligence. The new team is a mix of seasoned Grafanistas and new hires. We operate with a high degree of autonomy and ownership, both as individuals and as a team. Engineers are empowered to make decisions, move quickly, and validate ideas early – while being supported by a deeply collaborative culture that values curiosity, feedback, and cross-functional partnership. We’re looking for a Senior Software (Database) Engineer to help build the underlying storage system for our general purpose data platform. The database layer has both OLAP and OTLP capabilities to support classic analytics queries while also serving up freshly stored data. It embraces the separation of compute and storage and is designed to run on multiple cloud providers. This is an early-stage role on a high-autonomy team, so you should be comfortable working through ambiguity, making pragmatic architectural decisions, and building systems that can evolve from internal dogfooding to production-grade SaaS. As the team matures, there’s a broad opportunity to expand or redefine this role based on impact and initiative. What You'll Be Doing: Take ownership over and build one or multiple parts of the database: Design, implement, test, and operate ingestion, query planning, distributed query execution, data formats, storage formats. Create a scalable SaaS foundation: Help define and build the architecture for a multi-tenant database service, including tenant isolation, usage tracking, quotas, audit logs, background jobs, and reliable service boundaries. Power agent-facing retrieval workflows: Build APIs and database interfaces that allow AI agents, MCP tools, CLIs, and internal applications to retrieve data quickly. Work across product and infrastructure: Partner with the team to make practical tradeoffs between fast experimentation and long-term reliability, especially as the project moves from prototype to production. Operate what you build: Instrument database services with metrics, logs, traces, alerts, and dashboards. Use observability tools to understand system behavior and improve reliability. Contribute to technical direction: Help shape the architecture, service boundaries, storage choices, API contracts, deployment patterns, and engineering practices for a new product area. Effective communication: You’ll be working in a highly dynamic and collaborative environment, so we need someone who can communicate effectively and contribute across teams. Ownership and impact: Take full ownership of the database solutions you develop, ensuring they are not only innovative but also scalable, maintainable, and aligned with real user workflows. What Makes You a Great Fit: Strong engineering skills: Solid experience building production-grade, user-facing software systems. You’re a self-starter, capable of tackling complex engineering problems and making design decisions with minimal supervision. AI experience with a practical mindset: You’re familiar with AI technologies and frameworks, and you focus on delivering high-quality solutions that work in the real world, not just in theory. Quick iteration and experimentation: You’re comfortable releasing prototypes, collecting feedback, and iterating with a pragmatic mindset. Proven initiative: You take ownership and drive projects forward, pushing boundaries to find the most impactful solutions. You can deal with ambiguity and are able to define scope where things are loosely defined. Collaborative attitude: You communicate effectively with your peers. You’re open to feedback, and you bring a solutions-oriented mindset to the table. Experience with distributed systems, catalogs and table formats, query engines. Mastery of a programming language like golang, C++, or Rust Proven track record of delivering software that made it into production and is actively used by users. Exposure to working in cloud-native environments (e.g., AWS, GCP, Azure). Experience using observability tools to understand and troubleshoot system behavior. Bonus Points for: Experience building distributed query engines. Experience building data warehouses and/or data lakes. Experience building tools for data engineering. How we work: We are a remote-first team that meets regularly over video and does most of our work asynchronously, in writing. We value creativity, diverse perspectives, and clear communication. Tempo is relied upon by prominent global organizations to monitor critical applications and infrastructure, and we expect everyone on the team, including our Staff engineers, to contribute ideas that make it a more reliable, more useful, and more loved product. Compensation & Rewards: In the US, the Base compensation range for this role is $ 154,445 - $185,334. Actual compensation may vary based on level, experience, and skillset as assessed throughout the interview process. All of our roles include Restricted Stock Units (RSUs), giving every team member ownership in Grafana Labs' success. We believe in shared outcomes—RSUs help us stay aligned and invested as we scale globally. *Compensation ranges are country specific. If you are applying for this role from a different location than listed above, your recruiter will discuss your specific market’s defined pay range & benefits at the beginning of the process. Why You’ll Thrive at Grafana Labs: 100% Remote, Global Culture - As a remote-only company, we bring together talent from around the world, united by a culture of collaboration and shared purpose. Scaling Organization – Tackle meaningful work in a high-growth, ever-evolving environment. Transparent Communication – Expect open decision-making and regular company-wide updates. Innovation-Driven – Autonomy and support to ship great work and try new things. Open Source Roots – Built on community-driven values that shape how we work. Empowered Teams – High trust, low ego culture that values outcomes over optics. Career Growth Pathways – Defined opportunities to grow and develop your career. Approachable Leadership – Transparent execs who are involved, visible, and human. Passionate People – Join a team of smart, supportive folks who care deeply about what they do. In-Person onboarding - We want you to thrive from day 1 with your fellow new ‘Grafanistas’ to learn all about what we do and how we do it. Balance is Key - We operate a global annual leave policy of 30 days per annum. 3 days of your annual leave entitlement are reserved for Grafana Shutdown Days to allow the team to really disconnect. *We will comply with local legislation where applicable. Equal Opportunity Employer: Grafana Labs is an equal opportunities employer. We welcome applications from everyone regardless of race, colour, nationality, origin, caste, sex, gender reassignment identity or expression, sexual orientation, age, religion or belief, disability, veteran status, genetic information, pregnancy, maternity, marital, family or carer status, or any other characteristic which is protected by local law. We believe that equality and diversity build a strong organisation, and we work hard to ensure that is the foundation of our organisation as we grow. Grafana Labs may utilize AI tools in its recruitment process to assist in matching information provided in CVs to job postings. The recruitment team will continue to review inbound CVs manually to identify alignment with current openings. #LI-Remote For information about how your personal data is used once you’ve applied to a job, check out our privacy policy .

Задачи: — Разработка, создание и поддержка надежных конвейеров обработки данных и процессов ETL/ELT для поддержки различных источников данных, включая структурированные и неструктурированные данные, с обеспечением высокого качества и целостности данных. — Создание и управление масштабируемыми архитектурами данных, обеспечивающими частые преобразования и манипуляции данными в сложных средах с множеством источников. — Тесное сотрудничество с аналитиками данных, специалистами по обработке данных и другими заинтересованными сторонами для обеспечения соответствия конвейеров обработки данных потребностям бизнеса. — Мониторинг, устранение неполадок и оптимизация конвейеров обработки данных для обработки крупномасштабных и разнообразных наборов данных, включая системы обработки в реальном времени и пакетной обработки. — Обеспечение соответствия стандартам управления данными, безопасности и конфиденциальности, внедрение передовых методов отслеживания происхождения данных, контроля доступа и аудита. — Постоянное отслеживание новых технологий обработки данных, фреймворков ИИ и передовых методов для непрерывного улучшения инфраструктуры и рабочих процессов. Требования: — Разработка конвейеров обработки данных: Опыт создания масштабируемых автоматизированных конвейеров обработки данных с использованием методологий ETL/ELT, опыт работы со сложными преобразованиями данных и частыми манипуляциями с данными. — Хороший опыт работы с Databricks. — Источники и форматы данных: Опыт работы с широким спектром источников данных, включая реляционные базы данных, NoSQL, API, потоковые данные (например, Apache Kafka) и неструктурированные форматы данных. — Программирование и скриптинг: Уверенные навыки программирования на Python (pySpark, SparkSQL), SQL. — Большие данные и облачные платформы: Практический опыт работы с фреймворками для обработки больших данных (Apache Spark) и облачными платформами (IBM Cloud, AWS, Azure, Google Cloud) для масштабируемой обработки и хранения данных. — Автоматизация и оркестровка: Знание инструментов оркестровки рабочих процессов, таких как Apache Airflow или Dagster, для планирования, управления зависимостями и восстановления после сбоев. — Качество и управление данными: Особое внимание уделяется качеству данных, происхождению данных, соответствию нормативным требованиям (GDPR, CCPA) и передовым методам обеспечения безопасности, включая шифрование и управление доступом на основе ролей. — Решение проблем в сложных средах: Способность ориентироваться и решать проблемы, связанные с крупномасштабными, гетерогенными экосистемами данных, обеспечивая надежность и производительность конвейера обработки данных в сложных условиях.

Требования: – Коммерческий опыт работы в DevOps/SRE от 3 лет. – Уверенные знания и практический опыт работы с Kubernetes в production. – Helm, Docker, Nginx, Ingress. – Уверенное администрирование Linux. – Опыт построения и поддержки CI/CD, желательно GitLab CI. – Опыт работы с IaC: Ansible, Terraform. – Практический опыт с GitOps, желательно ArgoCD. – Опыт настройки мониторинга: Prometheus, Grafana, VictoriaMetrics. – Bash и/или Python для автоматизации. – Понимание принципов работы SQL и NoSQL баз данных: опыт с PostgreSQL, Redis, Qdrant будет плюсом. – Опыт работы хотя бы с одним из облаков: Yandex Cloud, Azure, AWS. – Опыт самостоятельного внедрения инфраструктурных решений у заказчиков. Будет плюсом: – Опыт работы сразу с несколькими облачными платформами. – Опыт построения инфраструктуры с нуля. – Понимание security-практик в DevOps. – Опыт работы с AI/ML-инфраструктурой и соответствующим стеком. – Опыт технического взаимодействия непосредственно с заказчиком.

Задачи: — Проектировать и автоматизировать средства контроля, механизмы обнаружения и инструменты для улучшения информационной безопасности инфраструктуры и продуктов Algolia — Исследовать, оценивать и рекомендовать новые технологии, методы и фреймворки информационной безопасности — Проектировать, внедрять и поддерживать системы мониторинга и устранения проблем информационной безопасности для защиты данных клиентов и систем Algolia — Сотрудничать с инженерными и продуктовыми командами для интеграции информационной безопасности в новые функции, системы и конвейеры разработки — Способствовать улучшению стандартов, процессов и лучших практик информационной безопасности в компании — Проводить оценку рисков и моделирование угроз для основных систем, сервисов и сторонних поставщиков — Участвовать и иногда возглавлять мероприятия по реагированию на инциденты информационной безопасности и анализу после инцидентов — Поддерживать текущие и новые инициативы по информационной безопасности и соответствию (например, SOC 2, Type II, ISO 27001, C5, GDPR) — Управлять и развивать программы публичного багбаунти и раскрытия уязвимостей Algolia Требования: — 3–6 лет опыта в инженерии информационной безопасности, защите инфраструктуры или смежных технических областях — Владение скриптингом или автоматизацией на одном из языков: Python, Bash, Go или аналогичных — Предпочтение опыту работы с Kubernetes — Глубокое понимание принципов информационной безопасности для современных облачных сред (AWS, GCP, Azure) и дата-центров — Опыт эксплуатации, настройки и управления системами логирования/SIEM, обнаружения угроз, управления позицией безопасности, EDR, SAST, SOAR, CTI и другими системами информационной безопасности, предпочтительно с опытом работы с Crowdstrike или Obsidian — Знание распространённых угроз, векторов атак и стратегий их смягчения — Хорошее понимание компьютерных систем, сетей и низкоуровневых протоколов с точки зрения информационной безопасности — Опыт обнаружения инцидентов, реагирования и управления уязвимостями — Отличные коммуникативные навыки для объяснения рисков и концепций информационной безопасности технической и нетехнической аудитории — Способность работать совместно с вице-президентом по информационной безопасности и самостоятельно достигать бизнес-целей — Свободное владение английским языком Желательно: — Опыт масштабирования программ информационной безопасности в быстрорастущих SaaS-компаниях — Сертификаты или эквивалентное обучение по облачной информационной безопасности (например, AWS Security Specialty, GCP Professional Security Engineer) — Опыт работы с системами управления секретами, такими как Hashicorp Vault — Участие в сообществах информационной безопасности, багбаунти, open-source инструментах или CTF — Опыт в области инженерии конфиденциальности, моделирования угроз или безопасного проектирования ПО Условия: — Гибкая модель работы с возможностью полностью удалённой или гибридной работы — Работа в международной команде с офисами в Париже, Нью-Йорке, Лондоне, Сиднее и Бухаресте — Конкурентная годовая зарплата в диапазоне £66,080–£91,800 — Культура, ориентированная на обучение, сотрудничество и инновации в области информационной безопасности — Поддержка профессионального развития и возможность влиять на безопасность систем, используемых миллионами пользователей

Задачи: — Реализация и поддержка систем CI/CD и решений для повышения продуктивности разработчиков. — Выполнение регулярной работы и срочного устранения неполадок в системе CI/CD Dremio. — Выявление областей для улучшения и внедрение изменений для повышения эффективности и надежности систем. — Решение проблем для инженеров Dremio, включая использование и создание плейбуков, документирование обходных решений и автоматизацию ручных процессов. — Работа с инфраструктурой во всех основных облаках (AWS, GCP, Azure). — Управление доступом к ключевой инфраструктуре. — Создание и поддержка CI/CD пайплайнов и связанных систем для повышения организационной эффективности. — Автоматизация локальных рабочих процессов разработки. — Анализ узких мест в производительности, выявление причин проблем, оформление дефектов, взаимодействие с разработчиками и документирование решений. — Поддержание стабильности веток разработки. — Автоматизация процессов релиза программного обеспечения. — Отчетность о состоянии, надежности и устойчивости систем CI/CD. — Активное участие в код-ревью. Требования: — Степень бакалавра или магистра в области компьютерных наук или смежной технической области. — Опыт работы не менее 1 года с распределенными системами большого масштаба. — Умение работать в быстро меняющейся среде стартапа. — Знание и опыт работы с хотя бы одной облачной платформой (AWS, Azure, GCP). — Базовое понимание облачных сетей и архитектуры. — Владение одним из языков программирования: Java, C++, Python, Go. — Знание Maven или аналогичных систем сборки. — Практический опыт автоматизации пайплайнов с использованием CI/CD инструментов, таких как Jenkins. — Опыт работы с Infrastructure as Code. — Опыт работы с Terraform, BASH, Docker, Jenkins и Kubernetes. — Умение писать технические отчеты, эффективно суммируя ключевые данные. — Страсть к обучению и внедрению решений с использованием новейших технологий. — Сильная ориентация на сотрудничество и командную работу. Будет плюсом: — Знание data lakes и data warehousing. — Владение SQL. Условия: — Удаленная работа. — Полная занятость.

Location: In-Office 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. Official Title - Center of Excellence Engineer Available Locations: Bengaluru, India About the Role:- As a Center of Excellence Engineer (Customer Solutions Engineer), you will provide technical guidance to Cloudflare customers. Working within the GTM organization and reporting to a Center of Excellence Leader, the Center of Excellence Engineer (CEE) helps Cloudflare customers and internal teams through structured onboarding, technical guidance, adoption support, and scalable enablement. You will guide customers as they move from provisioned accounts to a live, value-generating deployment. You are a Cloudflare product expert and will leverage your knowledge to ensure our customers understand and utilize the Cloudflare platform to its fullest extent. This is a customer-facing technical role focused on post-sale onboarding, technical advisory, 1:1 technical engagements and 1:many technical events. You will be collaborating with CEs, AEs, Support and other internal teams to ensure no customer falls through the cracks. Key Responsibilities: Customer Onboarding and Deployment Readiness: Own the end-to-end technical onboarding experience for assigned customers post-provision. Lead structured onboarding calls, guide customers to configure initial Cloudflare settings (e.g., DNS records, SSL/TLS modes, WAF rules, Zero Trust policies), and validate that traffic is behaving as expected before the customer is declared live. Guide customers through onboarding and implementation runbooks and identify when an engagement requires escalation to the appropriate Cloudflare team. Troubleshoot common onboarding issues including DNS resolution failures, SSL/TLS certificate errors, caching misconfigurations, and authentication failures. Cloudflare Configuration Guidance: Guide customers through Cloudflare configuration steps for applicable platform capabilities, including DNS, SSL/TLS, CDN, Application Security, Zero Trust/SASE, Network Services, and other Cloudflare products as assigned. Help customers understand configuration options, operational best practices, and standard deployment patterns. Customer Technical Guidance and Enablement: Deliver standardized customer guidance sessions, technical walkthroughs, workshops, bootcamps, 1:1 technical engagements and 1:many technical sessions covering Cloudflare product specific best practices, migration procedures, and post-deployment administration. Explain technical concepts clearly to both technical and non-technical customer stakeholders. Documentation and Engagement Hygiene: Produce and maintain clear technical and operational artifacts, including call summaries, action item trackers, onboarding plans, runbooks, and handoff materials. Keep relevant internal systems updated after customer touchpoints and maintain high-quality documentation for continuity across teams. Cross-Functional Coordination and Escalation Management: Coordinate with Cloudflare Customer Engineering, Support, Product, Engineering, Account, and other internal teams to route complex technical issues, document blockers, support escalations, and maintain continuity of customer outcomes. Operational Improvement and Knowledge Sharing: Contribute to reusable templates, procedures, technical guides, internal knowledge articles, automation scripts, AI-assisted workflows, and process improvements that improve the quality, consistency, and efficiency of customer onboarding and customer engineering activities. Technical Breadth and Continuous Development: Build and maintain working knowledge across Cloudflare product foundations, including Application Security, CDN, Zero Trust/SASE, and Network Services, at a depth appropriate to onboarding, adoption, troubleshooting, and technical advisory scenarios. Stay current on Cloudflare product releases, internal process updates, and relevant certifications. Be familiar with and leverage AI technology available to you to create new and innovative tools to support your work. Minimum Qualifications: 5-12 years of experience in a customer-facing technical role such as Customer Engineer, Technical Support, Implementation Engineer, Onboarding Engineer, Sales/Systems Engineer, Technical Account Manager, or NOC/SOC analyst or equivalent hands-on technical experience managing customer deployments. Cloudflare product foundations: working knowledge of Application Security, CDN, Zero Trust/SASE, and Network Services. Networking fundamentals: understanding of DNS, TLS/SSL, HTTP/HTTPS, CIDR, NAT, and BGP. Web security basics: familiarity with OWASP, WAF rules, OAuth, SAML, MFA, and Zero Trust principles. Configuration and integration: hands-on experience with the Cloudflare Dashboard, and comfort with Terraform, API-based configurations, or identity providers such as Okta, Azure AD, and SAML/OIDC integrations. Observability: ability to read and interpret Security Events, Logs, cURL outputs, and HAR files; awareness of common causes of 5xx/4xx errors post-onboarding. Scripting and APIs: ability to read and debug REST API calls; basic proficiency with Python and Bash. Working Style: Customer empathy: explains technical concepts without jargon; calms frustrated customers. Structured communication: clear agendas, concise summaries, documented next steps. Bias for action: progresses onboarding rather than waiting; knows when to escalate appropriately. Curiosity: digs into why something works and learns unfamiliar Cloudflare products quickly. Resilience: handles ambiguity, incomplete requirements, and changing priorities. Coachability: accepts feedback and improves from every onboarding engagement. Time management: juggles a portfolio of onboarding customers and meets committed timelines. Communication: Strong written and verbal communication skills. Able to document clearly, summarize concisely, and interact professionally with both technical and non-technical customer contacts. Regulatory and Security Awareness (Nice-to-Haves): Familiarity with compliance frameworks such as PCI DSS, HIPAA, SOC 2, or ISO 27001 is a plus. Strongly Preferred Qualifications (The Good-to-Haves): Cloudflare Certifications: Existing Cloudflare certifications (e.g., Cloudflare Fundamentals, Cloudflare for Teams, or Cloudflare for Developers). Prior Cloudflare Experience: Direct experience onboarding or administering Cloudflare in a production environment. Automation Mindset: Familiarity with Infrastructure-as-Code (Terraform, Pulumi) or CI/CD pipelines for configuration management. AI-Augmented Workflow: Comfort using AI assistants, LLM-based tools, or scripting to accelerate routine onboarding tasks, log analysis, or documentation. Fluency in English is required. 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: Remote, Singapore GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role As a Senior Manager, Customer Success Architects , you will lead a distributed Asia Pacific team of Customer Success Architects and Customer Success Managers that help customers realize value from GitLab through strong adoption, measurable outcomes, and trusted partnership. You will partner closely with both executive sponsors and day-to-day end users/practitioners to drive adoption and measurable outcomes. Reporting to the Sr Director of Customer Experience, APJ , you will coach Customer Success Managers & Customer Success Architects to support account health across onboarding, adoption stages, executive business reviews, and renewals, while also improving how the team works. This is a people leadership role with broad scope: you will develop talent, strengthen customer relationships, and partner across Sales, Product, Engineering, and other teams so customers are supported through growth, change, and escalation. What you'll do Lead a team of Customer Success Architects & Customer Success Managers focused on customer adoption, account health, customer satisfaction, and advocacy. Ensure delivery of the Success package entitlements and increase value by driving usage and consumption Coach team members through regular feedback, development support, and career growth conversations to strengthen knowledge, communication, and execution. Partner with the Sr Director of Customer Experience APJ and Sales stakeholders  to define team goals, clarify responsibilities, and align priorities with company direction. Manage resource assignments, staffing levels, and hiring activity to support team coverage and customer needs. Develop senior-level relationships with customers and support the team in building trusted advisor partnerships. Collaborate with Sales, Product Management, Engineering, Professional Services, Renewals and other internal teams to create a consistent customer experience. Support customers during escalations by working with other leaders to coordinate clear, effective responses. Improve the processes and tools used by the team and oversee initiatives tied to objectives and key results. What you'll bring Experience leading customer-facing delivery teams in software, technology, consulting, sales, or customer success environments preferably in DevOps or related domain. Ability to guide teams that drive software adoption and manage customer relationships across onboarding, adoption, business reviews, and renewals. Knowledge of software development lifecycle practices, related tools, and agile or DevOps ways of working. Familiarity with cloud or container technologies such as Kubernetes and Docker, application security concepts such as static application security testing (SAST) and dynamic application security testing (DAST), or cloud platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure. Skill in translating strategy into clear initiatives, coordinating execution, and tracking delivery across competing priorities. Capability to lead distributed teams across regions, cultures, and lines of business, while working effectively with senior executives and external stakeholders. Willingness to travel occasionally for customer meetings and team events, as needed. Strength in communication, coaching, feedback, delegation, planning, and process improvement. Warmth and care in your leadership approach—able to coach with empathy and build trust on a human level. Interest in candidates with transferable experience who can connect technical concepts to business value and collaborate well across functions. About the team On the Customer Success Management team, we are accountable for success bookings, customer adoption, measurable outcomes, customer satisfaction, and building customer advocacy across GitLab. We also act as a key connection point between customers and the broader GitLab ecosystem, helping streamline collaboration with Product Management, Engineering, Sales, Professional Services, and other partners. In this role, you will manage a team in Asia Pacific while collaborating asynchronously with stakeholders across regions, with a focus on helping customers succeed at scale and improving how the organization supports them. How GitLab Supports Full-Time Employees Benefits to support your health, finances, and well-being Flexible Paid Time Off Team Member Resource Groups Equity Compensation & Employee Stock Purchase Plan Growth and Development Fund Parental Leave Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application. Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process. Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us. GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law . If you have a disability or special need that requires accommodation , please let us know during the recruiting process .