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Location: Remote - United States Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . Team Description: The Ads Retrieval ML team builds the machine learning systems that identify relevant advertising candidates for Reddit users. Retrieval sits at the heart of the ads delivery funnel: before downstream ranking and auction decisions, our models determine which campaigns and ads are eligible to compete. We work on large-scale retrieval across multiple objectives, placements, and geographies. Our work combines representation learning, candidate generation, nearest-neighbor search, behavioral and contextual signals, and rigorous offline and online experimentation. Role Description: We are looking for a Staff Machine Learning Engineer to provide technical leadership for the Retrieval ML team. You will lead the design and evolution of retrieval models and modeling practices that improve relevance, advertiser outcomes, and user experience at Reddit scale. This is an applied ML role centered on retrieval modeling and end-to-end product impact. You will be expected to stay close to the technical details—from data and objective design through model development, evaluation, experimentation, and launch—while setting direction for other engineers. Responsibilities: Define the technical direction and multi-year roadmap for ads retrieval modeling in partnership with engineering, product, data science, and ads stakeholders. Design, develop, and launch candidate-generation and retrieval models for campaigns and ads across Reddit’s advertising surfaces. Apply modern approaches such as two-tower architectures, representation learning, embeddings, sequence models, graph-based methods, and other deep learning techniques when they create meaningful product value. Improve the retrieval stack across key modeling decisions, including objectives, labels, sampling strategies, hard-negative mining, feature design, embedding generation, candidate filtering, and retrieval depth. Work with approximate nearest-neighbor and vector retrieval systems, reasoning about recall, relevance, freshness, diversity, coverage, latency, and cost trade-offs. Establish strong evaluation practices that connect retrieval metrics—such as recall, precision, candidate coverage, calibration, and downstream lift—to ads and user outcomes. Lead offline analysis and online experiments, interpret ambiguous results, and translate findings into the next modeling iteration. Partner with downstream ranking, ads platform, auction, measurement, and product teams to ensure retrieval models integrate effectively into the full ads funnel. Write design documents, review code and model changes, and raise the quality bar for modeling, testing, observability, and production ownership. Mentor ML engineers and help grow the team’s expertise in retrieval, recommendation, and representation learning. Required Qualifications: 7+ years of industry experience, including substantial experience building and shipping applied ML products. Deep experience with information retrieval, candidate generation, recommender systems, ranking, or related relevance problems. Strong understanding of retrieval modeling concepts, including DNN, embeddings, two-tower or dual-encoder models, approximate nearest-neighbor search, and multi-stage retrieval. Deep experience training, evaluating, debugging, and deploying deep learning models using TensorFlow, PyTorch, or similar frameworks. Demonstrated ownership of ML projects from problem framing and data preparation through offline evaluation, online experimentation, production launch, and iteration. Strong command of experimental design and model evaluation, including how offline retrieval metrics relate to downstream business and user metrics. Experience working with large-scale behavioral, contextual, or content datasets and complex feature pipelines. Strong software engineering fundamentals and the ability to write clear, reliable, maintainable production code. Technical leadership experience: setting direction, leading complex projects, influencing partner teams, and mentoring other engineers. Excellent written and verbal communication, with the ability to explain complex modeling choices to technical and non-technical audiences. Preferred Qualifications: Experience with ads retrieval, ad serving, recommendation, search relevance, or marketplace optimization Experience modeling user, content, campaign, or ad interactions with sequential, graph, or multimodal signals Experience connecting retrieval improvements to downstream ranking, auction, conversion, revenue, or user-experience outcomes Experience in ads marketplaces at peer companies Publications, patents, or industry contributions in applied ML or ranking systems Experience with sequential modeling (e.g., RNNs, Transformers) Benefits: 100% remote opportunity (we have 4 office locations for hybrid/onsite work preference in NY, SF, LA and Chicago) Comprehensive Healthcare Benefits and Income Replacement Programs 401k with Employer Match Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support Family Planning Support Gender-Affirming Care Mental Health & Coaching Benefits Flexible Vacation & Paid Volunteer Time Off Generous Paid Parental Leave #LI-AJ1 Pay Transparency: This job posting may span more than one career level. In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/ . To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below. The base salary range for this position is: $230,000 — $322,000 USD In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews. During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable.  We will not sell your personal information or disclose it to any third party for their marketing purposes.  We will delete any recording of your interview promptly after making a hiring decision.  For more information about how we will handle your personal information, including our retention of it, please refer to our Candidate Privacy Policy for Potential Employees and Contractors . Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve.  Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.

Location: Zurich, Switzerland About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The Product In a rapidly evolving world, trust in AI depends on AI agents being grounded in fresh, verified real-world data. Search is the foundation that makes this possible. We are building an agent-native search platform designed specifically for AI systems rather than human users. Our product provides programmatic, low-latency, and observable search APIs that AI agents use to retrieve, filter, and reason over real-world information at scale. The Role We are looking for a Staff Software Engineer to work on the indexing and data processing layer of a novel search engine tailored for agentic AI consumption. In this role, you will focus on building systems that ingest, process, and organise massive volumes of data into efficient, queryable structures. You will work primarily on offline and nearline pipelines, ensuring that data is fresh, complete, and efficiently accessible by downstream retrieval systems. You will operate in an environment where throughput, scalability, and correctness are critical; designing systems capable of handling tens of gigabytes per second across continuously evolving datasets. In this position, your responsibility will be to: Design, implement, and operate large-scale indexing systems and data pipelines that sit at the core of our search infrastructure Develop and optimise indexing strategies balancing performance, freshness, and resource efficiency Work on storage formats, compaction strategies, and update mechanisms to keep data accessible and current Ensure reliability and predictability of pipelines under high-throughput conditions Build well-tested components with clear responsibilities and interaction contracts, while remaining flexible as the system evolves Define and implement observability primitives, including structured logs, metrics, and data quality signals across offline and nearline pipelines Monitor throughput, resource usage, and cost, and drive optimisations when business needs require it Collaborate with runtime and ML teams to ensure indexing outputs meet retrieval and ranking requirements Enable safe experimentation on indexing strategies and data processing logic through controlled rollouts and clearly defined quality signals You may be a good fit if you: 8+ years of experience building production backend or data infrastructure systems Strong Go experience (C++/Rust is a plus) Experience with large-scale data processing systems (10+ GiB/sec throughput, petabyte-scale datasets, etc.) Experience building or operating databases, storage systems, data planes, or indexing pipelines Strong understanding of distributed systems, fault tolerance, consistency, and scalability Experience running production systems and handling operational incidents Systems-thinking mindset and ability to reason about end-to-end data flows Strong candidates may also have experience with: Distributed data processing frameworks such as Spark, Flink, MapReduce, or Beam Content systems including, scraping, proxying, or anti-bot infrastructure Ad tech, social networks, or other large-scale content platforms DBMS internals (open source or SaaS) and cloud infrastructure Open-source contributions or active involvement in the engineering community Competitive programming or CTF participation (ICPC, IOI, or similar) SHAD or similar advanced technical programmes Conference talks or technical publications We conduct coding interviews as part of the process. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Talablar: — Kompyuterdan ishonchli foydalana olish; — Savodli yozma nutq; — O‘zbek va rus tillarini yaxshi bilish; — Mas’uliyatlilik va e’tiborlilik; — Yaxshi kommunikativ ko‘nikmalar; — Ma’lumotlarni tez o‘zlashtirish va turli vaziyatlarga moslasha olish. Ish holati: 10:00-19:00(5/2), offline.

Talablar: — Frontend bo'yicha 1-3 yillik ish tajribasi; — Web ilova va admin panel yaratish; — Yangi funksiyalar, API integratsiyasi, buglarni tuzatish va performance optimizatsiyasi; — Next.js App Router, SSR/SSG, SEO asoslari va AI vositalaridan samarali foydalana olish. Ish turi: — 10:00–19:00 (offline, 5/2)

Задачи: — создавать интерактивные сцены и 3D-объекты по визуальным референсам; — настраивать свет, анимацию и VFX; — работать с UI в Unity; — интегрировать и адаптировать сторонний контент; — участвовать в создании мультимедийных и интерактивных проектов. Требования: — коммерческий опыт с Unity от 3 лет, знание URP/HDRP; — уверенный high poly / low poly моделинг; — UV, PBR-текстурирование и baking; — опыт работы с UI, анимацией и VFX в Unity; — понимание level-art; — Substance Painter / Designer, Marmoset Toolbag, Photoshop; — реализованные коммерческие проекты и портфолио. Будет плюсом: Shader Graph / HLSL, риггинг и скиннинг, художественный бэкграунд, опыт с маппингом, offline rendering, Git/Plastic. Условия: — масштабные мультимедийные проекты в сфере edutainment, art, media и entertainment; — гибкое начало рабочего дня: 9:00 / 10:00 / 11:00; — проектную занятость на 2–3 месяца с возможностью перехода в штат; — очное присутствие в офисе в Москве.

About the Role: We are seeking a highly motivated and licensed Property & Casualty (P&C) Insurance Broker to join our team as part of a critical pilot to launch a P&C broker model at Gusto. You will be essential in evolving Gusto's business insurance channel by acting as a licensed agent to quote and issue business insurance policies with our NEXT Insurance carrier partner. You will use your expertise to provide direct support and an improved experience for small business customers with complex insurance needs, helping to capture meaningfully higher revenue and accelerating the development of our AI Insurance Advisor. About the Team: This team is crucial to solving customer experience gaps and optimizing our offline insurance channels. You will be a SME who is part of the "human-in-the-loop" workflows required to train and optimize Gus as an AI insurance advisor. Our collective vision is to be an expert advisor that supports small businesses through every stage of growth, with this pilot being key to establishing our digital insurance agency and licensed agent integration. Here’s what you’ll do day-to-day: — Make outbound dials to prospective and existing small business clients to assess their complex P&C insurance needs and recommend suitable coverage options. — Quote and issue Property & Casualty policies, primarily with the NEXT Insurance carrier, in our offline sales channel. — Achieve required daily, weekly, and monthly sales activity expectations. — Provide human-in-the-loop support to optimize the AI Insurance Advisor, Gus, and assist customers with more complex needs. — Maintain client relationships, manage policy renewals, and facilitate changes or endorsements to existing policies. — Act as a trusted liaison between clients and carriers, including assisting clients with reports of losses and claims settlement when necessary. — Actively work to cross-sell and up-sell additional P&C lines of coverage and strive to achieve monthly production goals for units and/or revenue. — Maintain accurate and up-to-date client records and policy documentation within CRM systems (SFDC workflow). — Use AI-powered workflows as a core part of how you sell: running assigned tools daily, surfacing friction and inefficiencies as you find them, and collaborating with your team to influence what gets built or improved next. — Stay current on industry trends, products, regulations, and underwriting guidelines to provide accurate advice and ensure compliance. Here’s what we're looking for: Experience in the field: — Proven experience in insurance sales or brokerage, with coverage level knowledge of commercial P&C products (e.g., Workers' Compensation, BOP, Commercial Auto, E&O). — 4+ years Licensing: — An active Property & Casualty insurance producer’s license Communication & Interpersonal Skills: — Excellent verbal and written communication, active listening, and strong interpersonal skills to build rapport and explain complex concepts clearly. Sales & Negotiation: — Strong sales and negotiation abilities with a proven ability to meet sales targets. Technical Proficiency: — Working knowledge of carrier quoting systems, proficiency in insurance software, and familiarity with CRM systems like Salesforce (SFDC). — Comfortable using AI tools and automation to enhance productivity, decision-making, and problem-solving. Mindset: — A demonstrated passion for delivering an incredible customer experience and genuine empathy for small business owners. Attributes: — Highly self-motivated, goal-oriented, and an entrepreneurial ownership mentality to thrive in a fast-paced, high-energy environment. AI Fluency: — Proven, day-to-day experience integrating AI into how you work - across writing, research, prep, and summarization as a core part of your workflow, with a track record of giving clear, useful feedback that helps improve your tools and processes. Compensation: — Cash compensation amount for this role is $109,390 to $134,000 OTE for Denver, Atlanta, Chicago & Las Vegas. — $102,855 to $126,000 OTE for Phoenix. — Final offer amounts are determined by multiple factors including candidate location, experience and expertise and may vary from the amounts listed above. Work Format: — Gusto has physical office spaces in Denver, San Francisco, and New York City. — Employees based in those locations will be expected to work from the office on designated days approximately 2-3 days per week (or more depending on role). — The same office expectations apply to all Symmetry roles, Gusto's subsidiary, whose physical office is in Scottsdale. — When approved to work from a location other than a Gusto office, a secure, reliable, and consistent internet connection is required. Equal Opportunity: — Gusto is an equal opportunity employer and does not discriminate based on legally protected characteristics. — Committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in the job application process. Security & Privacy: — Gusto takes security and protection of personal information seriously.

Location: San Francisco, CA, USA About the role Chime’s Machine Learning Platform (MLP) team builds and operates the infrastructure, tooling, and developer experience that powers machine learning across the company. We enable data scientists and ML engineers to develop, train, deploy, and monitor models reliably and efficiently. As a Senior Software Engineer on the Machine Learning Platform team, you will design and build scalable systems spanning traditional machine learning and emerging AI workloads, including model training, feature computation, real-time inference, foundation-model access, evaluation, and agentic orchestration. You’ll work at the intersection of distributed systems, cloud infrastructure, applied machine learning, and AI product engineering. This role focuses on creating secure, reliable, and reusable platform capabilities that help teams choose the right approach, from conventional predictive models to LLM-powered and multi-step agentic systems, while maintaining strong standards for evaluation, observability, governance, privacy, and cost efficiency. In this role, you can expect to Design, build, and operate scalable ML and AI infrastructure on AWS. Design and operate shared platform capabilities for LLM and agentic workloads, including model access, prompt and configuration lifecycle, retrieval, tool integration, state management, and workflow orchestration. Build evaluation frameworks for non-deterministic AI systems, including offline benchmarks, regression testing, online quality signals, human feedback, and failure analysis. Establish observability, reliability, and governance for models and agents, covering traces, model and prompt versions, tool calls, latency, token usage, quality, safety, privacy, and cost. Help teams make principled architecture decisions across traditional ML, LLM-powered applications, and agentic workflows, and contribute to the platform’s technical roadmap. Build distributed training, batch inference, and large-scale processing systems using frameworks such as Ray or Spark. Build and maintain infrastructure as code using Terraform. Support and evolve the feature store and feature pipelines. Develop data ingestion and streaming systems using technologies such as Kinesis, Kafka, Flink, or Spark. Improve CI/CD workflows for ML models, AI applications, and platform components. Partner closely with Data Science and ML Engineering teams to improve developer experience. Participate in on-call rotations to support production systems. To thrive in this role, you have Knowledge of the machine learning development lifecycle, including data preprocessing, model training, evaluation, deployment, and monitoring. Experience designing distributed systems and large-scale data or compute platforms on AWS using frameworks such as Spark or Ray. 5+ years of experience in ML or AI infrastructure, platform engineering, distributed systems, or production ML systems. Working knowledge of LLM application patterns such as retrieval-augmented generation, structured outputs, tool calling, agent orchestration, and evaluation of non-deterministic systems. Experience designing production systems that integrate ML or foundation models through reliable APIs, workflows, and data contracts. Hands-on experience with CI/CD pipelines, DevOps practices, and infrastructure as code. Experience with containerization and orchestration technologies such as Docker and Kubernetes. Strong programming skills in Python, Go, Scala, Java, or similar languages. Solid understanding of software engineering fundamentals, including testing, version control, code review, and observability. Nice-to-have Experience shipping LLM-powered or agentic systems to production. Experience with one or more of the following: model gateways, prompt lifecycle management, retrieval or vector search, tool execution, and agent orchestration frameworks. Experience building evaluation, tracing, and observability capabilities for non-deterministic AI systems. Familiarity with managed or self-hosted foundation model infrastructure, such as Amazon Bedrock, SageMaker, or equivalent platforms. Experience operating GPU-based workloads and optimizing training or inference performance and cost; CUDA experience is a plus. #LI-Onsite #LI-WW1 Below is the base salary offered for this role and level of experience. Full-time employees may also be eligible for bonus(es), competitive equity, and benefits. For commissioned roles, the base salary listed in this job description does not include incentive/variable pay. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience. Wage Notice $187,000 — $259,000 USD A little about us At Chime, we believe that everyone can achieve financial progress. We created Chime—a financial technology company, not a bank*—on the premise that core banking services should be helpful, easy, and free. Through our user-friendly tools and intuitive platforms, we empower our members to take control of their finances and work towards their goals. Whether it's starting a savings account, purchasing a first car or home, launching a business, or pursuing higher education, we're proud to have helped millions unlock their financial potential. We're a team of problem solvers, dreamers, and builders with one shared obsession: our members. From day one, Chimers have worked tirelessly to out-hustle and out-execute competitors to bring our mission to life. Their grit and determination inspire us to work harder every day to deliver the very best experience possible. We each bring an owner's mindset to our work, refusing to be outdone and holding ourselves accountable to meet and exceed the highest bars for our teams, our company, and our members. We believe in being bold, dreaming big, and taking risks, while also working together, embracing our diverse perspectives, and giving each other honest feedback. Our culture remains deeply entrepreneurial, encouraging every Chimer to see themselves as stewards of our mission to help everyday Americans unlock their financial progress. We know that to achieve our mission, we must earn and keep people's trust—so we hold ourselves to the highest standards of integrity in everything we do. These aren't just words on a wall—our values are embedded in every aspect of our business, serving as a north star that guides us as we work to help millions achieve their financial potential. Because if we don't—who will? *Chime is a financial technology company, not a bank. Banking services provided by The Bancorp Bank, N.A. or Stride Bank, N.A., Members FDIC. What we offer for our full-time, regular employees 🏢 Our in-office work policy is designed to keep you connected - with four days a week in the office and Fridays from home for those near one of our offices, plus team and company-wide events depending on location. Whether you’re coming in regularly or are part of our fully remote program, you’ll stay engaged with your work and teammates. 💻 Benefits that support your work and life, including backup child, elder, and pet care and subsidized commuter benefits for eligible employees. ✨ Comprehensive health, financial, and wellbeing benefits designed to support you at every stage of life. 🏝 Generous vacation policy and company-wide paid days off 🫂 1% of your time off to support local community organizations of your choice 👟 Annual wellness stipend to use towards eligible wellness related expenses 👶 Up to 22 weeks of paid parental leave for birthing parents and 12 weeks of paid parental leave for non-birthing parents 👪 Access to family planning reimbursement 💚 A challenging and fulfilling opportunity to join one of the most experienced teams in FinTech and help millions unlock financial progress We know that great work can’t be done without a diverse team and inclusive environment. That’s why we specifically look for individuals of varying strengths, skills, backgrounds, and ideas to join our team. We believe this gives us a competitive advantage to better serve our members and helps us all grow as Chimers and individuals. Chime is proud to be an Equal Opportunity Employer. We consider qualified applicants without regard to race, color, ancestry, religion, sex, national origin, sexual orientation, gender identity, age, marital or family status, disability, genetic information, veteran status, or any other legally protected basis under provincial, federal, state, and local laws, regulations, or ordinances. We will also consider qualified applicants with criminal histories in a manner consistent with the requirements of state and local laws, including the San Francisco Fair Chance Ordinance, Cook County Ordinance, NYC Fair Chance Act, and the LA City Fair Chance Ordinance, and consistent with Canadian provincial and federal laws. If you have a disability or special need that requires accommodation during any stage of the application process, please contact: accommodations@chime.com . To learn more about how Chime collects and uses your personal information during the application process, please see the Chime Applicant Privacy Notice .

Location: United States Mattermost is the leading collaborative workflow platform for defense, intelligence, security, and critical infrastructure. Trusted by the U.S. Department of War and Fortune 500s, our platform runs on-premises and in private clouds, delivering secure messaging, file sharing, workflow automation, audio/screenshare, and project management—all with full data and operational control. Mattermost powers high-stakes workflows across mission planning, real-time, real-world operations, DevSecOps, incident response, and cyber defense—enabling secure collaboration from tactical edge and DDIL environments to enterprise HQ. Teams operate across web, desktop, and mobile, with embedded interoperability for Microsoft Teams, Outlook, and Microsoft 365. To learn more, visit www.mattermost.com We are seeking a Senior Mobile Engineer to take ownership of the Mattermost mobile experience for teams working in defense, intelligence, and critical infrastructure. Our users operate in DDIL environments where connectivity may be disconnected, denied, intermittent, or limited, security is non-negotiable, and the software they depend on has to keep working. Our mobile app is primarily React Native and TypeScript today, with native iOS and Android integrations. Your immediate focus will be making that experience more reliable and performant. Over time, you'll also help shape the next generation of our cross-platform mobile architecture. You'll work hands-on across application and native layers, partner closely with platform and feature teams, work directly with customer-facing teams on field reports from constrained environments, and help raise the bar for how we observe, diagnose, and build mobile software. Responsibilities Reliability and Performance Architect the app for DDIL and other constrained network environments Improve data and functional reliability across connection lifecycle, synchronization, recovery, and failure scenarios Own crash-free and release-health outcomes, using telemetry to proactively identify regressions and drive them through diagnosis to resolution Profile and improve performance across startup, networking, storage, rendering, memory, and battery usage Mobile Platform Ownership Own critical iOS and Android integrations and the boundaries between native and cross-platform code Maintain and evolve platform capabilities including push notifications, background execution, secure storage, app lifecycle, and real-time communications Build within mobile distribution and device-management constraints, including App Store, enterprise and MDM deployment, AppConfig, and Intune MAM Diagnose production issues from native traces, telemetry, device profiling, and sparse field logs Make architecture recommendations that balance near-term delivery with the long-term direction of the mobile platform Craft and Delivery Mentor engineers on mobile architecture, reliability, performance, and platform best practices Uphold security and compliance standards required by government and enterprise customers Contribute to the open-source codebase Use AI coding tools effectively, applying strong engineering judgment to increase output without compromising reliability or quality What We Are Looking For We care about what you've built and the problems you've solved — not how long you've been building. If you have strong outcomes to speak to in mobile engineering, we want to hear from you. Required: Deep expertise in iOS or Android development, with strong working knowledge of the other platform Strong understanding of mobile architecture, platform SDKs, lifecycle constraints, and networking fundamentals Experience building reliable mobile systems that behave predictably through unreliable connectivity, local state changes, synchronization, and recovery Strong mobile performance profiling and production debugging skills on real devices Experience using production data, crash metrics, and release-health signals to drive reliability improvements Experience making architecture recommendations and owning technical outcomes Ability and willingness to work across native and cross-platform codebases AI fluency and experience using coding agents to increase engineering output while applying judgment to ensure reliable outcomes Preferred: Experience with React Native and TypeScript Experience with mobile persistence, synchronization, or offline-capable systems Experience with real-time communications or platform media frameworks such as WebRTC, CallKit, or Android audio APIs Experience with enterprise mobile distribution, MDM, AppConfig, or Intune MAM Experience building mobile software for security-sensitive, regulated, or operationally constrained environments Open-source contributions Why Mattermost Mission-driven work: Your contributions directly support the organizations and missions that depend on secure, reliable collaboration including the U.S. Department of War and critical infrastructure operators worldwide Remote-first culture: Work from anywhere with a globally distributed, high-trust team built for autonomy and ownership AI-forward environment: We actively adopt and build AI-enabled workflows. Agentic development is core craft here, not a novelty End-to-end ownership: Ship and own complex software across the full stack with real accountability for what you build Open source at the core: Be part of a vibrant developer community shaping the future of secure collaboration Target Salary Range: $165,000 – $215,000 Mattermost takes a market-based approach to pay and pay may vary depending on your location. The successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. These ranges may be modified in the future. Mattermost offers a remote-first work environment with medical, dental, and vision coverage. You’ll also receive unlimited PTO plus company holidays, a work from home stipend, an annual learning and development budget for conferences, certifications, and courses, paid parental leave for all parents, and a 401(k) plan. U.S. Eligibility & Compliance This role may require obtaining and maintaining a U.S. government security clearance. Candidates must meet federal eligibility requirements to be considered. Applicants must meet eligibility requirements for access to export-controlled information as defined by U.S. export control laws, including EAR and ITAR. For more information visit the Bureau of Industry and Security and the Directorate of Defense Trade Controls . Mattermost is an EEO Employer, we are a remote-first, open-source company. We are continually working to expand our hiring in more countries and regions, ensuring compliance with local laws and regulations, which takes time. Mattermost values your unique perspective—we welcome all applicants. We encourage individuals from all backgrounds to apply and are committed to assessing candidates based on their skills and qualifications. We do not tolerate discrimination against staff or applicants based on race, religion, national origin, age, disability, pregnancy status, veteran status, or other personal characteristics. If you require accommodations during the interview process, please let us know—we’re happy to assist.

Location: Remote, Canada; Remote, Poland; Remote, United Kingdom; Remote, United States 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. About the role We are looking for a Principal Engineer to help build GitLab Duo Agent Platform, the agentic orchestration layer that runs AI agents and flows across the entire software lifecycle. Principal Engineers at GitLab are recognized experts across multiple technology domains and represent the senior technical leadership within their organization. In this role you will set technical direction for a significant part of the Agent Platform, working across several teams to turn agentic capabilities into something engineering organizations can actually depend on: predictable, observable, governable, and fast enough to sit in the critical path of real development work. The Agent Platform is not a single service. It spans agent execution and orchestration, flow definition and event-driven triggering, the AI Catalog through which customers discover and manage agents, model routing and evaluation, and the governance surface that decides which agents may act where and under whose identity. It runs on GitLab.com, GitLab Dedicated, and Self-Managed installations, including deployments that use self-hosted models to meet compliance requirements. You will work on the parts of that system where the hard architectural questions live, and you will do it hands-on. This role reports into the VP of the AI Engineering organization and works closely with Engineering Managers, Staff Engineers, Product Management, Infrastructure and Security. What you'll do Define the technical direction for a significant area of Duo Agent Platform across several teams, and partner with Engineering Managers and Product to turn it into a roadmap with a six-month horizon. Build the orchestration layer that makes agents and flows reliable: session lifecycle, tool invocation, context assembly, concurrency, retries, timeouts, and failure modes that degrade gracefully instead of stalling a developer's workflow. Hands on work on driving topics throughout the whole organisation from POC level, architecture to full implementation. Participate in Incident Management on-call rotation, to help meet availability goals for GitLab.com. Strong product builder and ownership mentality. Understanding of product strategy, technical strategy, delivering customer value and iteration towards a successful product Write clear, opinionated design documents, architecture narratives, and decision records that let other teams make aligned decisions. Ensure the architecture holds across multi-tenant GitLab.com, Dedicated, and Self-Managed deployments, including self-hosted model configurations, and respects data governance boundaries in each. Use the platform on our own work, find where it falls short of what we claim, and feed that back through improvements into the product. Mentor Staff and Senior Engineers, and raise the bar for experimentation rigor and cross-team collaboration. Work across the whole platform on different projects, features and teams. This reaches across: Design the extensibility surface so customers can build custom agents and flows, connect their own tools and internal systems, and bring external agents such as Claude Code and Codex into GitLab workflows without weakening the platform's guarantees. Drive down the cost and latency of agentic requests, and work with Product on how model selection, routing, and credit consumption behave as usage grows. Define the evaluation practice for the platform: offline benchmarks and online experiments that measure task completion, correctness, tool-call accuracy, latency, and cost per task, and that catch regressions before customers do. Build the governance and safety surface with Security and Compliance: policy-driven controls over which agents and models may run where, permission and identity semantics that align to existing roles and group structure, human-in-the-loop checkpoints, traceability of every flow session, and audit that stands up to enterprise review. Complete Interview Training and participate in hiring for the department as a technical interviewer or panel member. Take part in on-call or operational support where your team’s ownership model requires it. What you'll bring 8+ years of software engineering experience, including 3+ years at Staff, Principal level or equivalent senior technical leadership. Hands-on production experience building on large language models: agent loops, tool calling, context management, retrieval, prompt and model evaluation. Experience shipping this to real users, not only prototyping it. Strong background in distributed systems, including service decomposition, fault tolerance, observability, and operational resilience under multi-tenant load. A track record of defining evaluation frameworks and using benchmark and experiment results to make architecture decisions, rather than shipping on intuition. Experience designing human-in-the-loop controls, safety guardrails, and responsible AI practices for systems that act on a customer's behalf. Ability to drive alignment across Engineering, Product, Infrastructure, and Security on problems that start out ambiguous. Experience mentoring senior engineers and shaping technical direction across multiple teams without direct authority. Comfort working in one or more of Ruby, Go, Python, or TypeScript, and willingness to work across the stack the platform spans. Excellent written and asynchronous communication, and the ability to work effectively in a fully remote, globally distributed organization. About the team The AI engineering organisation is building the product and infrastructure that make GitLab an effective environment for running agents across the software development lifecycle. We’re distributed across the world and partner with teams throughout GitLab to help the GitLab Duo Agent Platform become the backbone of GitLab's AI features. You will report directly to the VP of Engineering for AI and especially work together with all the teams, the engineering management and other principal engineers in the AI department. This is an opportunity to lead a consequential group as AI changes how software is built. The base salary range for this role’s listed level is currently for residents of the United States only. This range is intended to reflect the role's base salary rate in locations throughout the US. Grade level and salary ranges are determined through interviews and a review of education, experience, knowledge, skills, abilities of the applicant, equity with other team members, alignment with market data, and geographic location. The base salary range does not include any bonuses, equity, or benefits. See more information on our benefits and equity . Sales roles are also eligible for incentive pay targeted at up to 100% of the offered base salary. United States Salary Range $250,000 — $325,000 USD 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 .

Задачи: — функциональное и регрессионное тестирование Android-приложения; — анализ логов в Android Studio, проверка push и offline-сценариев, разных разрешений экранов; — анализ падений через Crashlytics; — тестирование веб-версии, кросс-браузерность и адаптивность; — проверка REST API в Postman; — тест-кейсы и чек-листы в TestRail, баги в YouTrack; — участие в планировании, грумингах и релизных приёмках. Требования: — 3+ года в manual QA; — 2+ года тестирования Android; — 1+ год тестирования Web; — опыт кросс-браузерного и адаптивного тестирования; — Android Studio + анализ логов; — Postman, TestRail, YouTrack; — системный подход и внимательность. Условия: — Официальное трудоустройство по ТК; — Формат офис в Ереване. График 5/2 (8 часов); — ДМС, оплачиваемые отпуска и больничные; — Компенсация обучения, английского и спорта; — Доп. 4 day-off в год.

Location: Limassol; Nicosia; Athens; Thessaloniki; Fully Remote - in other eligible locations Workplace: hybrid The Role:  We're seeking a Senior AI QA Test Automation Engineer to take a leading technical role in AI quality and test automation. You will work primarily with our AWS Bedrock AgentCore-based CX Agent Suite, a multi-agent customer-support system covering intent routing, FAQs, deposit-status queries, and human handoff. You will design and build the evaluation and QA tooling required to validate AI agents reliably, while also contributing to technical standards and best practices across QA.  This is a Senior Individual Contributor (IC) role with a strong technical and strategic focus. You'll work closely with QA leadership, Data Science, and Engineering to architect scalable AI evaluation systems, establish technical standards, and drive the evolution of our AI evaluation framework. Our current evaluation harness is based on DeepEval and is evolving towards a broader agentic evaluation architecture. You will have the opportunity to evaluate and prototype emerging agent-orchestration approaches, including Strands Agents, and contribute to architectural decisions around their adoption. The main responsibilities of the position include: Act as the primary technical enabler for QA, building scalable AI/ML frameworks, libraries, and tooling for the CX Agent Suite and beyond Design and build AI evaluation pipelines that assess the CX Agent Suite's multi-agent responses (Intent Detector, FAQ Agent, Missing-Deposits Agent) for accuracy, relevance, tone, hallucination rate, safety/guardrail compliance, and task completion — extending or replacing the current DeepEval-based evaluation harness Evaluate and prototype Strands Agents (or comparable agent-orchestration frameworks) for building self-evolving, autonomous QA agents, and drive the go/no-go decision on adoption Collaborate with QA, Data Science, and Engineering to integrate AI-driven testing into the existing GitLab CI/CD pipeline (dev → test → staging → prod), alongside Terraform-provisioned, EKS/AgentCore-hosted services Build resilient and adaptive automation that can detect and respond to changes in agent behavior, Bedrock Guardrails configuration, and routing logic Develop data-driven quality analytics, including root-cause analysis, quality trends, and intelligent test prioritization, leveraging existing observability and evaluation data from OpenTelemetry, CloudWatch, X-Ray traces, and LangFuse offline evaluation runs Lead research into emerging AI testing methodologies and mentor engineers through code reviews, workshops, and architectural guidance Main requirements: BSc/MSc in Computer Science, AI, or a related discipline 6+ years of hands-on experience in QA/Test Automation, with strong experience designing and maintaining automation frameworks 1+ years of experience applying AI/ML in software testing or QA process improvement, with a proven track record of bringing AI/ML solutions into production workflows Strong Python skills, with experience working in Python/AWS-centric technology environments; Java and/or TypeScript is a plus Hands-on experience with LLM evaluation techniques, including LLM-as-a-judge, human-in-the-loop evaluation, RAG, and multi-agent orchestration patterns Practical experience with DeepEval or comparable evaluation frameworks, along with familiarity with agent-orchestration SDKs such as Strands Agents Knowledge of LLM tooling, vector databases, and MLOps pipelines Experience integrating AI tooling into enterprise CI/CD environments, particularly GitLab, and working with containerized cloud-native environments such as Docker and Kubernetes/EKS Strong communicator, able to influence technical decisions and drive engineering standards across teams The following will be considered an advantage: Experience with autonomous QA agents or agentic orchestration frameworks for self-evolving test suites Experience with LLM observability tools such as LangFuse, LangSmith, or Arize, particularly for measuring probabilistic and adversarial robustness Knowledge of AI ethics, fairness, and bias detection for guardrail and model validation Experience with gRPC, WebSockets, and/or HTTP/2 Experience with AWS Bedrock, plus familiarity with GCP Vertex AI and/or Azure AI Benefit from: Attractive remuneration package Intellectually stimulating work environment Continuous personal development and international training opportunities The Hiring Experience: What Awaits You Let’s Connect – Intro Chat with Talent Acquisition Deep Dive – First Interview with Your Future Team Final Connection – Final Interview All applications will be treated with strict confidentiality!   By submitting this application, I confirm that I have read and agree to the terms of the Privacy and Data Protection Notice and the Confidentiality Undertaking.

Location: San Francisco; New York City Perplexity Computer is one of the defining products of the new era of agentic AI. Millions of people use Perplexity to transform knowledge into action, and the Agent Capabilities team sits at the intersection of frontier AI research and product innovation, building the foundations that shape how users and agents solve increasingly complex tasks. As every major breakthrough in AI models creates new possibilities, the Agent Capabilities team is responsible for turning frontier AI breakthroughs into reusable product capabilities. We are often the first to evaluate emerging model capabilities, determine where they create real user value, and transform them into reliable, scalable, high quality experiences for both users and agents. This is a highly leveraged role with broad ownership at the intersection of frontier AI research, agent systems, platform engineering, and product innovation. Tech Stack: Python | Go | Rust | PostgreSQL | DynamoDB | AWS | TypeScript WHY PERPLEXITY IS DIFFERENT - Craftsmanship. We build high quality, tasteful products targeting both the AI native and AI curious. - Ownership. You identify the problem, design the solution and ship it. - Entrepreneurship. We think like founders, act with urgency, and hustle to deliver for each other and our users. - Scholarship. Work among highly talented peers, pursuing knowledge and truth, upleveling ourselves, our teams, and our products. - Partnership. We amplify each others' strengths, break down silos, and give selflessly to help our colleagues deliver excellence. WHAT YOU'LL DO - Evaluate frontier models against real user tasks, identify useful behaviors and failure modes, and turn the most promising advances into production agent systems. Own the lifecycle from rapid prototyping and evaluation through launch, monitoring, and iteration. - Improve agents’ ability to plan, use tools, manage context, recover from errors, and complete long-running tasks reliably. - Apply state of the art ML and LLM techniques to design scalable agent capabilities such as skills, plugins, artifact generation, tools integrate and use, auto-research, and multi-agent collaboration. Shape the architecture, abstractions, and product experiences that enable both users and agents to compose increasingly sophisticated solutions for real-world tasks. - Own agent behavior and capabilities end-to-end, from user-facing products and interfaces to backend services. Define offline and online evaluations for task completion, correctness, safety, latency, cost, and user satisfaction. Iteratively improve across models, prompts, harnesses, and products for different problem spaces. - Build secure, observable, and reliable agent systems, including permissions and safeguards for sensitive actions. Develop tracing, replay, and monitoring infrastructure that makes agent failures reproducible and actionable. - Collaborate closely with PM, Data Science, Research, to identify high-impact opportunities in understanding and validating emerging model capabilities, and turn complex agent behaviors into simple, reliable product experiences. - Apply relevant advances in models, inference, evaluation, and agent architecture when they produce measurable improvements in production performance. Set technical direction on ambiguous problems and raise the bar through design reviews, mentorship, and technical leadership. QUALIFICATIONS - Typically 6+ years of professional software engineering experience, with a track record of building and owning robust AI-powered, large-scale, user-facing or data-intensive products. Exceptional candidates with less experience and an outstanding record of impact are encouraged to apply. - Strong software engineering fundamentals, with experience building and operating AI/ML products, backend services, or distributed systems at scale. - Experience owning the AI product lifecycle, including data analysis, rigorous evaluation, production monitoring, and iterative improvement. Able to define metrics and use production data and user feedback to guide decisions. - Practical experience in one or more relevant areas, such as agent harnesses, tool use, context engineering, model evaluation, browser automation, or long-running task execution. - Strong product judgment and execution: you can translate ambiguous user needs into applied AI or ML problems and ship durable solutions with measurable user impact. - Genuine interest in frontier AI capabilities, agent systems, and excitement for rapidly exploring, evaluating, and productizing new model behaviors. NICE TO HAVE - Experience with LLM context engineering or harness engineering, experience with subagents, coding assistants, long-running or autonomous task execution. - Deep familiarity with the strengths and limitations of current model families across reasoning, tool use, context management, and long-horizon tasks. - Experience building agent permissions, safeguards, evaluation infrastructure, or production observability systems. - Experience with mid-training, post-training, or reinforcement learning for frontier or open-source models, along with a strong understanding of model strengths and limitations across reasoning, tool use, context management, and long-horizon tasks. - AI/ML research experience demonstrated through publications, open-source contributions, or other meaningful research impact. - Time spent at a fast-growing startup or on a high-ownership engineering team.

Location: San Francisco; New York City Perplexity Computer is one of the defining products of the new era of agentic AI. Millions of people use Perplexity to transform knowledge into action, and the Agent Capabilities team sits at the intersection of frontier AI research and product innovation, building the foundations that shape how users and agents solve increasingly complex tasks. As every major breakthrough in AI models creates new possibilities, the Agent Capabilities team is responsible for turning frontier AI breakthroughs into reusable product capabilities. We are often the first to evaluate emerging model capabilities, determine where they create real user value, and transform them into reliable, scalable, high quality experiences for both users and agents. This is a highly leveraged role with broad ownership at the intersection of frontier AI research, agent systems, platform engineering, and product innovation. Tech Stack: Python | Go | Rust | PostgreSQL | DynamoDB | AWS | TypeScript WHY PERPLEXITY IS DIFFERENT - Craftsmanship. We build high quality, tasteful products targeting both the AI native and AI curious. - Ownership. You identify the problem, design the solution and ship it. - Entrepreneurship. We think like founders, act with urgency, and hustle to deliver for each other and our users. - Scholarship. Work among highly talented peers, pursuing knowledge and truth, upleveling ourselves, our teams, and our products. - Partnership. We amplify each others' strengths, break down silos, and give selflessly to help our colleagues deliver excellence. WHAT YOU'LL DO - Evaluate frontier models against real user tasks, identify useful behaviors and failure modes, and turn the most promising advances into production agent systems. Own the lifecycle from rapid prototyping and evaluation through launch, monitoring, and iteration. - Improve agents’ ability to plan, use tools, manage context, recover from errors, and complete long-running tasks reliably. - Apply state of the art ML and LLM techniques to design scalable agent capabilities such as skills, plugins, artifact generation, tools integrate and use, auto-research, and multi-agent collaboration. Shape the architecture, abstractions, and product experiences that enable both users and agents to compose increasingly sophisticated solutions for real-world tasks. - Own agent behavior and capabilities end-to-end, from user-facing products and interfaces to backend services. Define offline and online evaluations for task completion, correctness, safety, latency, cost, and user satisfaction. Iteratively improve across models, prompts, harnesses, and products for different problem spaces. - Build secure, observable, and reliable agent systems, including permissions and safeguards for sensitive actions. Develop tracing, replay, and monitoring infrastructure that makes agent failures reproducible and actionable. - Collaborate closely with PM, Data Science, Research, to identify high-impact opportunities in understanding and validating emerging model capabilities, and turn complex agent behaviors into simple, reliable product experiences. - Apply relevant advances in models, inference, evaluation, and agent architecture when they produce measurable improvements in production performance. Set technical direction on ambiguous problems and raise the bar through design reviews, mentorship, and technical leadership. QUALIFICATIONS - Typically 6+ years of professional software engineering experience, with a track record of building and owning robust AI-powered, large-scale, user-facing or data-intensive products. Exceptional candidates with less experience and an outstanding record of impact are encouraged to apply. - Strong software engineering fundamentals, with experience building and operating AI/ML products, backend services, or distributed systems at scale. - Experience owning the AI product lifecycle, including data analysis, rigorous evaluation, production monitoring, and iterative improvement. Able to define metrics and use production data and user feedback to guide decisions. - Practical experience in one or more relevant areas, such as agent harnesses, tool use, context engineering, model evaluation, browser automation, or long-running task execution. - Strong product judgment and execution: you can translate ambiguous user needs into applied AI or ML problems and ship durable solutions with measurable user impact. - Genuine interest in frontier AI capabilities, agent systems, and excitement for rapidly exploring, evaluating, and productizing new model behaviors. NICE TO HAVE - Experience with LLM context engineering or harness engineering, experience with subagents, coding assistants, long-running or autonomous task execution. - Deep familiarity with the strengths and limitations of current model families across reasoning, tool use, context management, and long-horizon tasks. - Experience building agent permissions, safeguards, evaluation infrastructure, or production observability systems. - Experience with mid-training, post-training, or reinforcement learning for frontier or open-source models, along with a strong understanding of model strengths and limitations across reasoning, tool use, context management, and long-horizon tasks. - AI/ML research experience demonstrated through publications, open-source contributions, or other meaningful research impact. - Time spent at a fast-growing startup or on a high-ownership engineering team.

Location: Remote Who we are Our mission is to bring clarity and control to the world's most complex codebases. AI is accelerating code creation, but the infrastructure to understand, oversee, and evolve that code hasn't kept pace. Sourcegraph gives engineering organizations full visibility across their systems, precise context for their agents, and the ability to execute coordinated code changes at scale. As agentic development becomes the dominant engineering paradigm, we provide the context layer teams need to take control of their codebase. With Code Search, Deep Search, MCP, and Agentic Batch Changes, we deliver on that mission today - giving engineering teams and their AI tools the cross-repo context to navigate massive codebases with confidence, and the ability to make changes across hundreds of repositories at once. Companies like Stripe, Reddit, and Leidos rely on Sourcegraph to ship faster and with higher quality. We're backed by a16z, Sequoia, and Redpoint, and proud to operate as a globally distributed team that values high agency, direct communication, and customer love. If you want to build the infrastructure that lets every engineering team - and every agent they deploy - operate on their codebase with confidence, join us. Hours & location 🌎 While we hire almost anywhere in the world, we have a preference for someone to reside in the following locations for this role. However, if you feel qualified, we welcome you to apply regardless of location. No matter what, working hours must overlap with EST for at least 20 hours/week. Preferred locations: USA Why this job is exciting Sourcegraph is at the forefront of building AI tools to solve the biggest problems in the software industry, problems that only get bigger as codebases grow and as more of the work is done by agents. The Code Understanding team owns the surfaces where that intelligence meets the developer: Deep Search , our agentic, multi-step answer engine across an enterprise's entire lineup of codebases, Query Assist , turning natural language queries into Sourcegraph query syntax, Smart Hovers , concisely summarizing symbols right where devs need it, guided diff review, and the APIs that both humans and AI agents rely on every day. Most of what makes those surfaces tick is agent engineering : a blend of software engineering, machine learning, and statistics. Agent engineering tells us which model to use and when, how to retrieve and pack context, how to measure answer quality, where to fine-tune or distill a smaller model to cut costs and latency, and how to expand a single LLM call into a reliable multi-step agent. As the staff ML and agent engineer on Code Understanding , you'll be the technical owner of it: setting the tam’s direction for models, evaluations, and agentic systems, making our products measurably better, faster, and cheaper, and raising the team's fluency in building with models. This is a staff-level role : we're hiring a technical leader, not just a strong individual contributor. The primary need is a production ML and evaluation authority who also builds production agent systems. You'll own the hardest, most ambiguous problems in this space, set standards others follow, and influence direction beyond your immediate team. You'll get the exhilarating chance to drive the vision on how we can provide the best code understanding experience on the market by combining our deterministic, large-scale systems and AI into experiences never seen before. Concretely, you'll own work like: Agentic systems. You'll design and harden the multi-step, tool-using agent loops behind current and new agentic experiences, turning research and experiments into reliable, observable, and affordable products at enterprise scale. Pragmatic use of evaluations. Crafting agentic products means needing to tell when a change actually helped, which is hard when agents keep changing and the product keeps shifting. You'll bring judgment about where evaluations earn their keep, when to use targeted smoke tests and metrics, and how to avoid noise dressed up as rigor, so we can move fast with confidence. Models: selection, upgrading, and training. You'll decide which models we run where, drive upgrades, and fine-tune our own when that's the right call. Retrieval and context engineering. You'll push on how we ground models in a customer's code - retrieval, ranking, context windows, citations - to make answers more accurate and verifiable. Cost and latency. Every surface has a per-user economic budget. You'll treat cost and latency as product features, and profile, distill, cache, and right-size models so we can ship ambitious features sustainably. You'll do this on a small, senior-leaning team that ships quickly, owns a lot of product surface, and has streamlined product management: engineers here talk to customers, frame the problem, and own it end-to-end. You'll have real agency over technical direction and a direct line to the impact of your work. 📅 Within one month, you will… Get the Code Understanding products and their model/agent pipelines running end-to-end locally, and land your first improvements to a model, prompt, retrieval path, or eval. Build a clear picture of where the AI engineering pain is, and the product surfaces most constrained by them. Get to know the team and our customers, and start forming your own opinions about where our agentic products should go next. Join the team's on-call support rotation. 📅 Within three months, you will… Own a meaningful agentic slice of the product end-to-end, driving it from problem-framing through rollout and measurement. Establish how the team ships model and prompt changes responsibly: the evals, dashboards, and guardrails that make quality and cost regressions visible before customers feel them. Begin up-leveling teammates in building with models by pairing with them, reviewing their code, and modeling good agent engineering instincts. 📅 Within six months, you will… Be the recognized technical authority for agent engineering and agentic systems on Code Understanding. You will be the person teammates, and increasingly the wider department, defer to on model, eval, and agent-design decisions. Have measurably moved the products: better answer quality, lower cost/latency, or new agentic capabilities that weren't feasible before. Be setting the direction of the team's roadmap where it intersects agents, bringing conviction, backed by evidence, about which bets are worth making, and pulling other engineers up to execute on them. About you You are a staff engineer and technical leader with hard-won skills across production machine learning, evaluation, and agent systems . This high-leverage role relies on your ability to make sound model and evaluation decisions for a fast-moving product, build the production systems around them, steer technical direction, and be a force multiplier for a talented, product-minded team. You're equally comfortable reasoning about an eval harness, a fine-tuning run, a retrieval pipeline, and the multi-step agent loop that ties them together, and you make everyone around you better at all of it. You operate at staff scope: you own the most ambiguous, highest-risk problems in your domain, go into whatever codebase a problem requires, set standards and patterns others adopt, and translate fluidly between engineering goals and business objectives. You influence direction beyond your immediate team. You lead through technical excellence and mentorship. You have personally owned a production model lifecycle. You have trained or fine-tuned at least one model and taken it from dataset construction through evaluation, production rollout, and monitoring. You can explain how you chose between training or fine-tuning and prompting or retrieval, how you chose baselines and metrics, what error analysis revealed, and how production evidence affected the next version. You build agents, fluently and opinionatedly. You've designed multi-step agentic systems and made them reliable, observable, and cost-bounded. You have a point of view on where agents shine and where deterministic code or human judgment is required. You have strong evaluation judgment. You build representative datasets, meaningful baselines, useful error taxonomies, and release criteria that connect offline measurements to production behavior. You know where rigorous evaluations earn their keep, where lightweight smoke tests or qualitative review are enough, and where a precise-looking metric is misleading. You treat cost and latency as product constraints. You make measured quality, latency, and cost tradeoffs and use the appropriate combination of model selection, prompting, retrieval, caching, distillation, and fine-tuning rather than reaching reflexively for a more complex model. You operate autonomously on ambiguous problems. Given a rough product idea, a few customer quotes, and a Slack thread, you come back with a plan, a prototype, milestones, and a point of view on tradeoffs, without it being pre-scoped. You own high-technical-risk projects end-to-end. You contribute beyond your domain. As a senior IC, you go into whatever part of the codebase a problem requires, recognize issues beyond your immediate area, and translate between engineering goals and business objectives. You up-level the people around you. You mentor by pairing on hard problems, providing substantive design and code reviews, and spreading agent engineering literacy across the team. You see investing in your teammates' growth as part of the job. You're customer and product-driven. You're comfortable on customer calls and in feedback threads, you turn raw signals into requirements, scopes, and milestones, and you push back when feedback would lead the product astray. You're pragmatic, not a perfectionist. You ship the smallest correct thing, prefer robust solutions over complicated ones, and keep a high-quality bar with simplicity. On the engineering fundamentals: You're a strong software engineer who can ship production services. You're comfortable across our stack - Go on the backend, TypeScript on the frontend, GraphQL, Postgres, Docker - or you're clearly able and eager to get there. You're fluent with agentic coding tools, and you understand and own every line it submits. You're comfortable in an async-first, multi-service, fast-paced remote environment. Nice-to-haves: You've shipped an LLM-powered or agentic developer-facing product you can speak about opinionatedly: what worked, what didn't, what you'd do differently. You've fine-tuned, distilled, or trained models to meet cost, latency, or quality targets in production. Experience with retrieval, ranking, embeddings, or search relevance. Experience working directly with enterprise customers and translating their needs into a product. Experience mentoring or up-leveling engineers, especially raising a team's agent engineering fluency. Level 📊 This job is an IC4. You can read more about our job leveling philosophy in our Handbook. Compensation 💸 We pay above-market salaries because we want to hire exceptional people who can focus on building great products, not worrying about paying bills. As an open and transparent company, our compensation philosophy and pay bands are visible to every Sourcegraph teammate, and we strive to make our approach equitable, explainable, and competitive. Your base salary is determined by the IC4 pay band for your location zone (1-4). Our pay bands are informed by market data and designed to ensure competitive compensation wherever you live. During the recruiting process, we'll discuss the range applicable to you based on job level, relevant skills, experience, qualifications, and location zone. 💰 The starting salary for the IC4 pay band in each zone is: Zone 2: $176,000 USD Zone 3: $132,000 USD Zone 4: $88,000 USD 📈 In addition to competitive cash compensation, we offer meaningful equity (because when Sourcegraph succeeds, we want you to succeed, too) and generous perks & benefits . Interview process Below is the interview process you can expect for this role (you can read more about the types of interviews in our Handbook). It may look like a lot of steps, but rest assured that we move quickly and the steps are designed to help you get the information needed to determine if we’re the right fit for you… Interviewing is a two-way street, after all! We expect the interview process to take 5.5 hours in total. 👋 Introduction Stage - we have initial conversations to get to know you better… [30m] Recruiter Screen [30m] Hiring Manager Screen 🧑‍💻 Team Interview Stage - we then delve into your experience in more depth and introduce you to members of the team, including cross-functional partners… [60m] Resume Deep Dive [60m] System Design [60m] Harden a Code Research Agent Interview [60m] Cross-functional team collaboration / Values 🎉 Final Interview Stage - we move you to our final round, where you gain a better understanding of our business and values holistically… [30] Leadership We check references and conduct your background check Please note - you are welcome to request additional conversations with anyone you would like to meet, but didn’t get to meet during the interview process. Learn more about us You can learn more about what it is like to work at Sourcegraph by reading our handbook . We are an ambitious team who are collectively working hard to build the most influential company in the world. You can read more about our culture , competitive compensation and benefits here. Sourcegraph is an equal opportunity workplace; we welcome people from all backgrounds. Sourcegraph participates in E-Verify for U.S. Employees.

Location: Belgrade, Serbia; Berlin, Germany; Limassol, Cyprus; Madrid, Spain; Munich, Germany; Paphos, Cyprus; Prague, Czech Republic; Remote, Germany; Warsaw, Poland; Yerevan, Armenia If you want to build and own the systems that decide what JetBrains customers are entitled to use and bill them accurately for it, this is for you. You will focus on designing backend services, keeping complex commercial flows correct, and tackling hard distributed systems challenges on the critical path of every paid request. About JetBrains We create intelligent software development tools for developers and teams. More than 15 million users, over 300,000 companies, and 88 of the Fortune Global Top 100 rely on our products to solve real, complex problems. Our mission is simple: make development teams more productive and AI adoptable at scale. What you’ll do At JetBrains, the Cloud Platform department builds the underlying infrastructure that powers authentication, paid capabilities, usage metering, and accounting. Within this department, the Licensing & Billing team sits right at the center of our commercial model, turning what a customer buys into what they are actually allowed to use, and accounting for that consumption accurately enough to bill on. The complexity of our entitlement, quota, and metering systems continues to grow. In this role, you will own the correctness and evolution of these backend services, take full ownership of end-to-end data flows, tackle distributed consistency across unreliable upstream systems, and ensure revenue-affecting data migrations happen cleanly without taking services offline. Day to day, you will: Design, build, and operate backend services that decide what a customer is entitled to and what their usage costs. Own flows end to end, from ingesting licensing and purchase data from upstream systems to enforcing quotas and recording consumption. Evolve our domain model to handle new commercial structures, combining seat-based plans and usage-based models. Design integrations with external systems, ensuring correctness through idempotency, ordering, reconciliation, and eventual consistency. Plan and execute live data migrations using feature flags, dual writes, backfills, and verification at every stage. Build observability into our systems by defining invariants, metrics, and alerts to catch silent divergence before customers do. Design internal APIs with explicit focus on versioning, backward compatibility, and developer experience for other JetBrains teams. Improve system performance and reliability under continuous production load while decommissioning legacy subsystems. Write integration tests against real dependencies and maintain reliable test infrastructure. Investigate production incidents, find root causes, and follow through on lasting fixes. What you’ll bring An engineer’s dedication to accuracy, especially in domains where system errors have direct financial impact. Pragmatic judgment when designing distributed systems where correctness under unreliable delivery is the main constraint. A habit of trusting metrics, traces, and logs to understand production behavior rather than relying on assumptions. Comfort navigating and simplifying large existing codebases containing legacy decisions. Clear, direct communication skills to agree on API contracts across teams with different priorities. A focus on testing as an essential design tool rather than an afterthought. What you’ll need A proven track record of building and operating backend services on the JVM platform using Kotlin or Java. A solid working knowledge of relational databases, including transactions, isolation levels, locking, indexing, and live schema migrations (PostgreSQL preferred). A deep understanding of distributed systems patterns, such as idempotency, retry strategies, eventual consistency, and data reconciliation. Practical experience making live data migrations or system changes without downtime using feature flags and staged rollouts. Experience designing versioned, backward-compatible APIs for other engineering teams. Hands-on experience with event-driven architectures and message brokers like Kafka. The ability to diagnose, debug, and resolve production incidents in complex environments. Proficiency in both written and spoken English. What success looks like Success in this role means building a bulletproof entitlement and metering foundation that product teams can trust without hesitation. You will have successfully migrated live commercial systems to support new billing models without downtime or data divergence. Silent errors will be caught automatically by system invariants before affecting users, and internal teams will integrate seamlessly with your clean, versioned APIs. Why join JetBrains? Strong base salary. We offer competitive pay that reflects your skills and experience. Flexible work location. Enjoy the freedom to work from home or from the office. Remote work. Spend up to 30 days per year working remotely from abroad. Extra time off. More days to relax, recharge, and do the things you love. Medical insurance allowance. Enjoy peace of mind for you and your family Learning and development opportunities. Access to conferences, courses, and language classes. Relocation support. We help make your move as smooth and stress-free as possible. (keep if relevant for the role) Language classes. Pick up the local language or sharpen your English skills. Fuel your day. Enjoy a hot meal or receive a lunch allowance on workdays. Mental health support. To help you feel your best, we provide easy access to professional mental health services. Sports benefit. Enjoy an on-site gym or sports club stipend. Internal events. Join company-wide celebrations and team gatherings. *Some benefits may vary depending on location. #LI-KT1 We are an equal opportunity employer We know great ideas can come from anyone, anywhere. That’s why we do our best to create an open and inclusive workplace – one that welcomes everyone regardless of their background, identity, religion, age, accessibility needs, or orientation. We process the data provided in your job application in accordance with the Recruitment Privacy Policy.

Задачи: — Функциональное и регрессионное тестирование Android-приложения — Анализ логов в Android Studio, проверка push и offline-сценариев, разных разрешений экранов — Анализ падений через Crashlytics — Тестирование веб-версии: кросс-браузерность и адаптивность — Проверка REST API в Postman — Тест-кейсы и чек-листы в TestRail, баги в YouTrack — Участие в планировании, грумингах и релизных приёмках Требования: — 3+ года в manual QA — 2+ года тестирования Android — 1+ год тестирования Web — Опыт кросс-браузерного и адаптивного тестирования — Опыт работы с Android Studio, анализ логов — Опыт работы с Postman, TestRail, YouTrack — Системный подход и внимательность Условия: — Официальное трудоустройство по ТК — Офис в Ереване, график 5/2 (8 часов) — ДМС, оплачиваемые отпуска и больничные — Компенсация обучения, английского и спорта — Доп. 4 day-off в год

Требования: — Опыт работы в тестировании ПО (Web, Backend); — Опыт в автоматизации тестирования с использованием Java, Selenide, RestAssured (можно до года); — Опыт тестирования систем на микросервисной архитектуре; — Опыт работы с базами данных; — Опыт тестирования REST API (ручное и автоматизированное); — Опыт работы с системой версионного контроля GIT (бранчи, пулл-реквесты и тд.); — Опыт работы с системами CI; — Умение использовать тестовые инструменты (Swagger, Postman, Fiddler/Charles, DevTools). Задачи: — Проведение ручного и автоматизированного тестирования мобильных и веб приложений; — Автоматизация тестирования с использованием Java, Selenide, RestAssured, Appium; — Тестирование API и микросервисов; — Проведение функционального и нефункционального тестирования мобильных и веб приложений, включая анализ и тестирование требований; — Написание тестовой документации, подготовка тестовых сценариев для ручного и автоматизированного тестирования; — Разработка фреймворка для тестирования; — Выявление/приоритезация ошибок и сообщение о них в системе отслеживания ошибок, выполнение RCA; — Проведение регрессионного, интеграционного тестирования. Условия: — Возможность поработать на проектах крупного Бигтеха; — Белая зарплата, трудоустройство по ТК РФ; — 31 день оплачиваемого отпуска в год; — Активная корпоративная культура с Online и Offline мероприятиями; — Добровольный ДМС (50% оплата от компании); — Корпоративные внутренние курсы: автоматизация тестирования на Java, нагрузочное тестирование; — Частичная компенсация пройденных курсов на различных платформах; — Добровольная международная сертификация ISTQB с компенсацией от компании; — Корпоративные занятия английским языком; — Оборудование от компании; — Реферальная программа "Приведи друга";

Location: United States Workplace: Remote At ClickUp, we're building the future of work: the first truly converged AI workspace unifying tasks, docs, chat, calendar, and enterprise search, all supercharged by context-driven AI. We are an AI-native company. Every team member is expected to leverage AI daily, and we evaluate AI fluency as part of our hiring process. Join us and help redefine what's possible. 🚀 Machine Learning Engineer, Ranking & Retrieval ClickUp is seeking an experienced Machine Learning Engineer to join our Search team. You'll own the ML systems that power search relevance for millions of users and ground our AI in the right context. Your Impact at ClickUp You'll own the full ML lifecycle for ranking and retrieval, from training through deployment and production serving. Our search indexes large-scale, user-generated content across a multi-tenant platform where permissions-aware retrieval is critical. You'll build the systems that decide what surfaces first. Core Responsibilities - Train, deploy, and serve ranking models in production, owning the full ML lifecycle - Build ranker features, training pipelines, and offline evaluation frameworks - Design and scale hybrid retrieval combining lexical and vector search (including HNSW with disk offloading) - Run embedding inference at billions-of-documents scale - Improve query understanding through intent modeling and query expansion - Build permissions-aware retrieval that respects multi-tenant boundaries - Create measurement frameworks to evaluate and improve search quality - Collaborate with Search Infrastructure, AI, and backend teams to integrate ranking improvements across the platform Technical Requirements - Bachelor's degree in Computer Science, Machine Learning, or related field - 5+ years of ML engineering experience focused on ranking, retrieval, or information retrieval - Proven full ML lifecycle ownership: training, deploying, and serving models in production - Hands-on ranker model training: feature engineering, pipelines, offline evaluation - Experience building hybrid (lexical + vector) retrieval systems - Experience running embedding inference at large scale - Strong query understanding fundamentals: intent modeling, query expansion Preferred Qualifications - Permission-aware retrieval and multi-tenancy experience - Indexing large-scale user-generated content (not small or static datasets) - Hands-on experience with OpenSearch or Elasticsearch - Sharding, index management, and real-time ingestion at scale - Background in NLP, semantic search, or agentic retrieval - Experience with TypeScript in backend systems   Join Our Mission At ClickUp, we're committed to building the future of productivity software. As our Search Engineer, you'll play a crucial role in shaping the core functionality of our platform and driving our AI initiatives forward. Join us in our mission to make the world more productive! #LI-REMOTE Equal Opportunity Employer ClickUp is an Equal Opportunity Employer, and qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, or national origin. Privacy Notice ClickUp collects and processes personal data in accordance with applicable data protection laws. You can find further details by viewing our Global Candidate Privacy Notice. https://clickup.com/careers-terms/candidate-privacy-notice If you are a Philippine Job Applicant, please also see our Philippine Data Privacy Notice https://t333.s.clickup-attachments-stg.com/t333/d9a53ce2-6d2c-48cd-84b1-ed4e525f9613/Philippine%20Data%20Privacy%20Notice_2024.pdf?view=open for further details. Visa Sponsorship Please note we are unable to sponsor or take over sponsorship of an employment visa for roles outside of engineering and product at this time. Sponsorship for engineering and product roles is not guaranteed, but is instead based on the business needs for that specific role at that time. Please reach out to the recruiter with any questions. Fraud Alert ClickUp Talent Acquisition will only initiate contact via an @clickup.com http://clickup.com email or through our official careers portal on clickup.com http://clickup.com. We will never request fees, payments, or sensitive personal information. Please disregard any offers received outside these channels and report them to support@clickup.com. AI Processing Notice ClickUp may use artificial intelligence and machine learning technologies to help review and screen candidates' employment applications against role-related criteria. These tools support, but do not replace, human decision‑making. If you have questions or need an accommodation in the recruitment process, please contact us at AskPeople@ClickUp.com.