Вакансии

Задачи: — Автоматизация тест-кейсов — Сопровождение и поддержка существующих автотестов в актуальном состоянии — Запуск автотестов и анализ отчетов — Модернизация и оптимизация фреймворка автоматизации тестирования — Работа с документацией (Confluence, Swagger, ТестОпс) Требования: — Коммерческий опыт автоматизации тестирования веб-приложений (SOAP, REST) на языке Python от 1 года — Знание теории и методик тестирования ПО — Знание и применение ООП и основных паттернов проектирования в автоматизации тестирования — Знание HTTP, REST, SOAP и клиент-серверной архитектуры, микросервисной архитектуры — Знание SQL (на уровне join) — Опыт разработки автотестов (pytest + requests + allure) — Опыт работы с Git — Опыт использования CI/CD GitLab — Опыт работы с брокерами сообщений Kafka, ActiveMQ — Опыт работы с системами логирования Kibana, Grafana Плюсом будет: — Опыт работы Docker, K8s — Работа с 1C Условия: — Официальное трудоустройство в аккредитованную IT-компанию — ДМС — Удаленный формат работы (предоставляем технику, если нужно) — Возможности обучения и развития за счет компании — Можно работать из офиса в СПБ с современным дизайном и лучшими видами на Финский залив, зонами отдыха с выходом на крыше

Location: AU: Sydney (45 Clarence St) Workplace: Hybrid ENGINEERING MANAGER - APPLIED AI THE ROLE AND ITS IMPACT You will lead and grow the Internal AI Accelerator team as it expands from its current core to a fully formed engineering squad. You will own the strategy, delivery, and roadmap for how Xero designs and delivers agentic AI workflows that eliminate manual toil, speed up decision-making, and generate measurable productivity gains. This means working directly with business leaders across G&A, Finance, P&C, and IT functions to co-build reusable AI patterns and governance frameworks that last. You will shape how Xero thinks about AI delivery long after your tenure, building playbooks and reference architectures that other teams can pick up and run with. What your engineers build goes into production and real people rely on it. One month you are partnering with finance, the next you are embedded in IT. You set the pace, you unblock the path, and you hold your team accountable to shipping. THE TEAM AND HOW THEY CONNECT The Internal AI Accelerator team is a small, high-impact crew of engineers embedded across Xero's business units. They partner directly with product, engineering, and business teams to design and ship AI workflows in weeks, not months. The team operates with a direct line to senior stakeholders and a genuine mandate to move fast. Together, you are genuinely curious about what works and what doesn't, learning openly from every project and sharing what you discover. THE TEAM IS CURRENTLY WORKING ON The team is expanding from a proof-of-concept phase into building at scale. You will focus on growing the engineering capability across multiple workstreams. Initially, your priorities will be: - Scaling agentic AI delivery across multiple business domains (finance, HR, support, operations) - Building reusable patterns, platforms, and governance frameworks that other teams can adopt - Designing and implementing the technical architecture for enterprise AI deployments - Establishing best practices for AI safety, evaluation, and operations at scale WHERE AND HOW YOU CAN WORK This role is based in Sydney. You will work in a hybrid model with core collaboration days in the office. Xero supports flexible ways of working, and regular "boost days" bring our ANZ teams together for connection and planning. HERE ARE SOME OF THE THINGS WE ARE LOOKING FOR - You bring solid engineering credibility, ideally from shipping production software at scale, so you can make sound technical decisions and earn your team's respect without needing to code day-to-day. - Your approach to strategy is pragmatic: you can move fast without all the answers, turning vague problems into clear scope, and pivoting when the situation changes. - You will Go Bold in how you lead: giving your team ownership, pushing back on processes that slow you down, and creating space for innovation alongside delivery. - You demonstrate genuine curiosity about your people and a commitment to coaching them up. You leave your team knowing more than when they joined. - You show comfort bridging technical and non-technical worlds, translating between engineers and business stakeholders without losing nuance on either side. Apply even if your experience isn't a perfect match! At Xero, we hire based on your skills, passion, and the unique perspective you can bring to enhance our culture and team.

Задачи: • Manual & automated testing • Design, execute and automate test cases • Develop automated test scripts, frameworks and tools • Perform regression testing • Identify and report bugs • Work closely with developers and product teams • Contribute to QA strategies and process improvements Требования: • Bachelor’s degree in Computer Science, Software Engineering or a relevant field • 3+ years of proven experience as a QA Engineer • Exceptional attention to detail and strong analytical skills • Experience with test management software • Familiarity with Agile frameworks and regression testing • Solid knowledge of Development Life Cycle and Defect Life Cycle • Strong time management and organizational skills • Excellent written and verbal communication skills Условия: • Competitive salary + performance-based bonus • 7.5-hour workday + 30-minute break • Up to 25 days of annual leave + public holidays • Professional development & training • Modern office in Paphos • Friendly team

Location: Москва Команда занимается разработкой «AI-помощника» - это проект с использованием LLM, который призван радикально улучшить клиентский опыт при при оформлении основных продуктов банка (Автоиндустрия) и помочь клиенту разобраться в этих продуктах. Для решения новых задач по развитию «AI-помощника» в «Сбербанк Онлайн» и поверхностях экосистемы СБЕРа мы ищем к нам в команду инженера по тестированию. Обязанности ### Обязанности * проверка точности рекомендаций и подсказок помощника * оценка качества диалогового взаимодействия и реакции на нестандартные запросы * мониторинг производительности системы при обработке сложных сценариев общения * проведение функционального/регрессионного тестирования помощника * разработка тестовой модели (составление и актуализация тест-планов и тест-кейсов) * обнаружение, заведение и отслеживание дефектов в Jira * автоматизация функционального/регрессионного тестирования * взаимодействие с разработчиками, аналитиками, смежными командами, сопровождением * организация и проведение приемо-сдаточных испытаний разработанных решений * разработка/поддержание документации по разрабатываемым системам. * разбор обращений клиентов в рамках 3-линии поддержки * участие в запуске и разборе автотестов в перспективе Требования ### Требования * уверенное знание методологии тестирования ПО * опыт тестирования WEB-сервисов от 2 лет (frontend & backend) * опыт тестирования микросервисов и API от 2 лет * опыт использования и понимание основ тестирования AI-агентов * опыт написания автотестов на Python (pytest, requests, playwright) * понимание принципов REST-архитектуры * опыт работы и ведения тестовой модели (тест-кейсы, тест-планы, тест-раны, покрытие, etc.) * понимание основных принципов CI/CD и инструментов * навыки работы с SQL (Составление простых запросов) * опыт работы с системами учета дефектов (Jira или аналогами) ### Будет плюсом * опыт работы в банковском секторе * опыт тестирования AI, чатов или чат-ботов Условия ### Наши условия * гибридный формат в Москве (Кутузовский пр, 32) * ежегодный пересмотр зарплаты и годовая премия * расширенный ДМС и льготное страхование для семьи * уникальная система обучения Сбера для профессионального и карьерного развития * выгодная ипотека для сотрудников * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера * корпоративная пенсионная программа.

Location: Hong Kong, Hong Kong SAR; Singapore, Singapore Who We Are At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom. OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). OKX is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves. Across our multiple offices globally, we are united by our core principles: We Before Me , Do the Right Thing , and Get Things Done . These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er. OKX is part of OKG, a group that brings the value of Blockchain to users around the world, through our leading products OKX, OKX Wallet, OKLink and more. About the Opportunity We are looking for an experienced blockchain developer to join our team building the next generation of high-performance on-chain trading infrastructure. You will help design and implement a unified trading engine — supporting Perpetuals, Spot, and Prediction Markets under a single margin account — and contribute to ExchangeOS , an infrastructure layer that lets KYC'd institutions and Web3 entities stake to create and operate their own trading venues (DEXs) on top of our chain. What You’ll Be Doing Design and build a unified account and matching system that supports Perpetuals, Spot, and Prediction Markets — including on-chain order matching, cross-margin validation, settlement, and liquidation/risk engines (ADL, ADL-avoidance, ADL waterfall, ecosystem insurance fund mechanics). Build and optimize the core components of ExchangeOS : the staking/venue-creation framework that allows KYC-verified institutions and Web3 protocols to launch and operate their own dex on shared infrastructure. Drive performance engineering across the stack — parallel transaction execution, low-latency order pipelines, and throughput/latency optimization at the engine and chain level. Track cutting-edge blockchain and trading-system technology (e.g. Hyperliquid, dYdX, other high-performance perp DEX architectures) and apply best practices to our infrastructure. Partner with product and business teams to translate venue/exchange requirements into technical designs, and provide ongoing technical support. What We Look For In You Strong proficiency in Rust , with hands-on experience in high-performance, low-latency systems; experience with parallel/concurrent execution engines is a strong plus. Deep understanding of exchange/matching engine mechanics: order book design, margin systems, liquidation and ADL, settlement, and risk engines — familiarity with perpetual, spot, or prediction market products is a strong plus. Solid grounding in blockchain fundamentals (consensus, P2P networking, cryptography); experience reading core code of major L1/L2 systems (Ethereum, Optimism, Arbitrum, or high-performance L1s like Hyperliquid/Solana) is a plus but not the primary focus of this role. Strong systems-performance mindset: profiling, memory/allocator tuning, and designing for sub-millisecond latency at scale. Excellent teamwork, communication, and logical reasoning skills; genuine passion for trading systems and blockchain technology. Perks & Benefits Competitive total compensation package L&D programs and Education subsidy for employees' growth and development Various team building programs and company events Wellness and meal allowances Comprehensive healthcare schemes for employees and dependants More that we love to tell you along the process! Notice: All official OKX vacancies are published on this website. While roles may appear on selected third-party platforms from time to time, information on other sites may be inaccurate or outdated. If in doubt, please apply directly through our official careers website. Information collected and processed as part of the recruitment process of any job application you choose to submit is subject to OKX 's Candidate Privacy Notice .

NVIDIA is hiring PCIE Verification Engineers with at least 1 year of experience.

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 At Anthropic, we're building AI systems that are safe, beneficial, and transformative. Our mission is to develop AI that benefits humanity, and we believe the most powerful capabilities emerge when we thoughtfully bridge the gap between research breakthroughs and real-world applications. Claude is getting good at research. Given a well-posed problem, it can read the literature, write and run simulations, analyze results, and iterate on a design across physics, materials, chemistry, and engineering. But most of the hardest problems in science and engineering aren't well-posed. They live inside companies and labs with their own data, tools, constraints, and experts, and progress depends on turning all of that into work a model can actually do and check. Our Applied AI, Science & Engineering team takes Claude to the scientists and engineers working on hard problems in energy, materials, hardware design, and other physical-world fields. We build the tools  Claude needs to do real research and engineering work. Mostly that means software: the agent harnesses, integrations, evaluations, and working processes that turn a partner's problem into something Claude can make progress on and validate. We find real-world problems and test what we build through partner engagements, then feed our learnings back to our research and product teams. It's early, and the engineers who join now will shape how Claude gets applied to science and engineering. This is an engineering role first. You'll spend real time with domain experts, become the person who knows how to get Claude working in their field, and prototype quickly alongside them. Most of your time, though, goes into building and hardening the systems that make that work repeatable, not into writing recommendations or configuring someone else's product. A science or engineering background is a big plus, but what matters most is that you can earn the trust of expert researchers, turn a fuzzy problem into something concrete and checkable, and ship. Responsibilities Build the agent harnesses and research loops that let Claude carry a problem from literature review through hypothesis generation, simulation, analysis, and design iteration Connect scientific computing and simulation tools (finite element, CFD, electromagnetic, or molecular modeling codes, for example) so Claude can drive them reliably and at scale Design and build evaluations that tell us whether Claude's work is actually right, and be honest about where it isn't Sit with scientists and engineers at partner organizations to learn their problem, data, tools, and constraints, and turn open-ended questions into well-specified, verifiable tasks with clear success criteria Prototype with partners, ship pilots into their workflows, and cut anything that doesn't move the problem forward Turn what works in one engagement into shared tools, reusable components, and documented processes the next one can start from Be the technical voice on how Claude performs on science and engineering work, explaining results, limits, and tradeoffs clearly to researchers, engineering leads, and non-technical stakeholders alike Partner with our research and product teams to share where models fall short on science and engineering work, and help shape what comes next You may be a good fit if you Have 8+ years of experience building software, with strong engineering fundamentals and comfort across the stack, from data pipelines to tooling to quick interfaces Have built real systems with large language models, including agents, tool use, or evaluations Have worked on technical problems in a science or engineering field, such as physics, materials, chemistry, or electrical or mechanical engineering Have a track record of zero-to-one work in startup or startup-like environments\ Can work directly with expert users, understand their workflows deeply, and still keep the focus on building the right system rather than the one first requested Have good judgment about what can and can't be verified, and are comfortable saying a result isn't good enough yet Bring high agency, pick up new domains quickly, and hold strong opinions loosely Communicate clearly with researchers, engineers, and external partners, and care about the societal impacts of your work Strong candidates may also have An advanced degree or research experience in a physical science or engineering field Hands-on experience with scientific computing and simulation, numerical methods, or optimization Experience in industrial R&D, a national lab, or a deep-tech company in areas like energy, materials, manufacturing, or hardware Experience building alongside customers or technical partners, for example in forward-deployed, applied AI, or solutions engineering roles, ideally where you also owned the software that came out of it Experience with ML research, RL environments, or evaluation design Candidates need not have 100% of the skills listed above Formal certifications or education credentials Expertise in every scientific domain we work in Direct machine learning or AI research experience Deadline to apply: None. Applications will be reviewed on a rolling basis. 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: $405,000 — $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. 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: Cyprus; Georgia; Poland; Spain; Serbia; Latvia We are looking for a Senior DevOps / Platform Engineer to develop and operate GoMining's cloud platform. The primary focus of this role is GCP and Kubernetes , infrastructure and application delivery automation, and the reliability and security of production services. You will maintain and improve our existing infrastructure, contribute to the development of the next generation of our platform, and help product teams ship changes safely and efficiently. Responsibilities Develop and operate infrastructure in GCP , including GKE, virtual machines, networking, load balancers, storage, and managed services. Manage Infrastructure as Code with Terraform : develop modules, manage state and dependencies, ensure reproducible changes, and identify configuration drift. Operate Kubernetes : upgrade clusters and platform components, configure autoscaling, workload placement, network policies, and resource allocation. Develop CI/CD and GitOps using GitLab CI and Flux / Argo CD, automating builds, checks, deployments, controlled releases, and rollbacks. Build observability across metrics, logs, distributed tracing, and actionable alerting. Define and improve SLIs/SLOs together with engineering teams. Troubleshoot production incidents and performance issues, conduct root cause analysis, and eliminate recurring failure patterns. Implement least-privilege access, environment and service isolation, secure secrets management and rotation, and infrastructure audit controls. Maintain backup processes and regularly validate recovery of critical data and infrastructure according to agreed RPO/RTO . Help engineering teams prepare services for production, including health checks, resource configuration, telemetry, and deployment procedures. Optimize cloud infrastructure usage and costs while maintaining reliability requirements. Maintain infrastructure documentation, operational runbooks, and recovery procedures. Requirements Strong hands-on experience independently operating production infrastructure and Kubernetes , including upgrades, troubleshooting, and disaster recovery. Strong experience with GCP , including GKE, Compute Engine, VPC, IAM, and Cloud Storage; good understanding of Cloud SQL, Cloud Logging, and Cloud Monitoring. Hands-on experience with Terraform , including modules, remote state, locking, importing existing resources, and safely applying infrastructure changes through CI. Deep knowledge of Kubernetes and containerization : Deployments, StatefulSets, Services, Ingress, storage, requests/limits, probes, autoscaling, RBAC, and NetworkPolicy. Experience with Helm and Kustomize . Strong Linux administration skills, preferably Debian/Ubuntu: processes, systemd, filesystems, disks, and resource troubleshooting. Strong networking fundamentals: TCP/IP, DNS, HTTP(S), TLS, routing, NAT, load balancing, and firewalls. Ability to troubleshoot connectivity issues across applications, Kubernetes clusters, networks, and cloud services. Experience building CI/CD pipelines, preferably with GitLab CI , and strong understanding of Git and GitOps principles. Understanding of runner permissions, secrets protection, and production deployment controls. Automation skills with Bash and Python , including maintainable infrastructure scripts and API integrations. Experience with monitoring, logging, and alerting using tools such as Prometheus, Grafana, or Cloud Monitoring . Practical experience operating PostgreSQL and understanding Redis and RabbitMQ, including availability, connections, replication or clustering, backup, and recovery. Strong understanding of cloud security principles: least privilege, service accounts, Workload Identity, secrets management, network segmentation, and access auditing. Ability to independently deliver infrastructure changes to a validated production result, explain technical trade-offs, and collaborate effectively with Engineering and Security teams. Nice to Have Experience with Flux CD, Argo CD, Ansible, and HashiCorp Vault . Experience with OpenTelemetry and distributed tracing. Experience migrating infrastructure between clouds or Kubernetes clusters, including database migrations with limited downtime. Experience designing disaster recovery strategies and conducting recovery exercises. Cloud cost optimization and capacity planning experience. Experience with DigitalOcean, Hetzner, or AWS . Ability to read and troubleshoot JavaScript / TypeScript applications. Experience operating financial or payment services with strict requirements around access control, auditing, reliability, and data integrity. Benefits Professional growth: support for courses, conferences, and English learning (up to 100% coverage). Work-life fit: remote or hybrid format with flexible hours across international teams. Paid leave: up to 20 vacation days +  8 company holidays + 5 personal days per year Recognition programs: structured performance reviews and team awards. Team culture: retreats in international locations (for example, company apartments in Cyprus).

Location: United Kingdom As a Pre-Sales Engineer, you will be responsible for securing the technical 'win' in Mid-Market and Enterprise client organizations, inspiring them to choose Action1. The ideal candidate is results-oriented and highly driven, with a strong focus on both personal and team success, aiming for exceptional professional growth. Key responsibilities: Drive deals to closure through pre-sales support and technical account management. Conduct technical qualifications of prospects by thoroughly understanding their business and technical needs. Deliver client-specific product demonstrations. Support our sales team and customers primarily in the EMEA regions Requirements Essential system administration experience, with the ability to assist IT administrators in recognizing Action1 as their preferred patching solution. 1-3 years of B2B pre-sales experience (preferred but not mandatory). Strong understanding of IT administration and security principles. Ability to work both independently and collaboratively as part of a team in a fast-paced, dynamic environment. Genuine interest in information technology and systems administration, with a passion for staying updated on industry trends. Benefits Fully remote work, giving you the flexibility you need in the modern world A multitude of interesting challenges and opportunities. Non-stop professional growth and learning of new technologies. Stable income and flexible working hours, opportunities for promotion. Friendly and professional peers.

Основные требования: — Знает SDLC, жизненный цикл дефекта, виды тестирования: smoke, regression, retest, exploratory, positive/negative. — Читает требования и описание изменений от разработчика, задаёт уточняющие вопросы до начала тестирования, а не после. — Пишет чек-листы и тест-кейсы, понятные другому QA; применяет граничные значения и классы эквивалентности. — Оформляет дефект так, чтобы разработчик воспроизвёл его без вопросов: шаги, expected/actual, окружение, версия, роль, скриншот или видео, запрос из Network. — Отличает баг от особенности требований и не заводит дубли. Техническая база: — Web: DevTools — Network, Console, cookies, local/session storage; клиент-серверное взаимодействие. — Figma: открыть макет, сверить UI с дизайном (отступы, шрифты, цвета, состояния), посмотреть значения в Dev Mode, оставить комментарий. — API: Postman/Swagger — HTTP-методы, status codes, headers, JSON, auth token. — SQL: SELECT, WHERE, ORDER BY, LIMIT, простой JOIN для проверки данных после действия в UI. — Git: получить актуальную ветку; branch, commit, merge request. — Linux и логи: SSH, базовые команды (cd, ls, cat, less, tail -f, grep); найти и прочитать nginx access/error logs и user logs на лог-сервере по времени и пользователю. — Docker: поднять готовое окружение через docker compose, посмотреть контейнеры и их логи (docker ps, docker logs). — Automation: запуск готовых Playwright-тестов, чтение отчёта; отличает баг от падения из-за окружения. — Mobile: установка сборок через TestFlight и Firebase App Tester, проверка на 2+ устройствах; логи через adb logcat и Xcode/Console, запись экрана; просмотр трафика через Fiddler. — AI-инструменты: для черновиков чек-листов и тестовых данных — с обязательной самостоятельной проверкой. Чем предстоит заниматься. Процессы и ответственность: — Ведёт задачу по workflow команды: от тестирования до закрытия или возврата с комментарием. — Возвращает задачу, если по ней недостаточно информации для тестирования. — Участвует в smoke, regression и проверке после обновления по чек-листу; со временем участвует в приёмке релизов вместе с наставником. — Сообщает о блокере в течение дня, а не в конце спринта. — Проходит основные маршруты документа своего продукта от создания до исполнения под разными ролями. — Проверяет подписание ЭЦП в вебе и через Documentolog Agent с тестовыми ключами, включая негатив: неверный пароль, просроченный или чужой ключ. — Проверяет права: пользователь без доступа не видит и не открывает документ, в том числе по прямой ссылке. — Проверяет интерфейс на казахском и русском языках. Взаимодействие: — Открыт к feedback; сначала пробует разобраться сам (15–30 минут), потом спрашивает с тем, что уже проверил. — Пишет в задачах коротко, по делу и без эмоций. Будет плюсом: — Любой язык программирования на базовом уровне (TypeScript предпочтительнее), опыт mobile или API automation. — Знакомство с СЭД, ЭЦП, eGov или интеграционными продуктами. Главный критерий уровня: самостоятельность + техническая глубина + влияние на качество продукта. Что мы предлагаем: — Работа с собственным продуктом и сложными техническими задачами. — Возможность развиваться в направлении архитектуры, производительности и высоконагруженных систем. — Сильная и поддерживающая команда, где можно получать помощь и обмениваться экспертизой. — Беспроцентные займы - финансовая поддержка сотрудников со стажем работы в компании от 1 года. — Фантомные акции - возможность получать процент от чистой прибыли компании после 1 года работы. Количество фантомных акций увеличивается с каждым годом работы в компании. — Премия раз в полгода - по результатам выполнения KPI. — Ежегодная индексация заработной платы.

Location: San Francisco, CA Workplace: OnSite ABOUT US Sierra is the leading platform for customer-facing AI agents, working with many of the world's biggest brands — including The GAP, Rocket Mortgage, SoFi, Sutter Health, and SoftBank — to transform how they serve customers and grow their businesses. We are primarily an in-person company based in San Francisco, with growing offices across North America, Europe, and Asia. We are guided by a set of values that are at the core of our actions and define our culture: Trust, Customer Obsession, Craftsmanship, Intensity, and a commitment to balancing Family along the way. These values are the foundation of our work, and we are committed to upholding them in everything we do. Our co-founders are Bret Taylor https://www.linkedin.com/in/brettaylor/ and Clay Bavor https://www.linkedin.com/in/claybavor/. Bret currently serves as Board Chair of OpenAI. Previously, he was co-CEO of Salesforce (which had acquired the company he founded, Quip) and CTO of Facebook. Bret was also one of Google's earliest product managers and co-creator of Google Maps. Before founding Sierra, Clay spent 18 years at Google, where he most recently led Google Labs. Earlier, he started and led Google’s AR/VR effort, Project Starline, and Google Lens. Before that, Clay led the product and design teams for Google Workspace. Sierra’s Applied Research team advances the quality, speed, and cost efficiency of the models powering our agents across a wide range of tasks. Our work spans post-training open-weight models for specialized capabilities and pioneering new approaches to voice AI, turning research breakthroughs into reliable production systems. WHAT YOU’LL DO - Tackle unsolved applied AI and ML problems. Pushing the boundaries of post-training, voice AI, and long-horizon task execution, building agents that can reason, adapt, and reliably carry out complex tasks for customers. - Determine when to build specialized models and when to use externally provided frontier intelligence, balancing quality, speed, and cost to deliver customer value. - Turn ambiguous product and customer problems into well-framed machine learning problems. - Develop, fine-tune, evaluate, and deploy models that improve Sierra’s customer-facing AI agents. - Advance our post-training work, from supervised fine-tuning toward reinforcement learning and long-horizon optimization. - Build rigorous evaluations that connect model performance to real-world agent quality. - Work with large-scale, proprietary datasets spanning text and audio interactions. - Collaborate across Sierra while taking ideas from experimentation through production deployment and customer impact. WHAT YOU’LL BRING - Strong experience building and deploying production machine learning and AI systems. We welcome backgrounds across applied ML, including search, ranking, recommendations, advertising, language, and speech. - Excellent judgment in framing product problems as ML tasks and selecting the simplest & most effective approach. - Experience working deeply with data, experimentation, evaluation, and model iteration. - A practical, evidence-driven approach to ML, with strong judgement around when a problem calls for modeling and when a simpler solution will deliver the best outcome. - The ability to operate with high agency and work across the stack when needed to deliver an outcome. - Alignment with Sierra’s values, including Craftsmanship, Customer Obsession, and Competitive Intensity. - A degree in computer science, machine learning, or a related field, or equivalent professional experience. EVEN BETTER… - Experience with supervised fine-tuning or reinforcement learning. - Experience in search, ranking, recommendations, or advertising systems. - Experience training and deploying language, speech, or multimodal models. - Experience designing model evaluations tied to real-world product outcomes. - Experience working with large-scale consumer datasets or high-volume ML products. - Contributions to applied ML research or benchmarks. OUR VALUES - Trust: We build trust with our customers with our accountability, empathy, quality, and responsiveness. We build trust in AI by making it more accessible, safe, and useful. We build trust with each other by showing up for each other professionally and personally, creating an environment that enables all of us to do our best work. - Customer Obsession: We deeply understand our customers’ business goals and relentlessly focus on driving outcomes, not just technical milestones. Everyone at the company knows and spends time with our customers. When our customer is having an issue, we drop everything and fix it. - Craftsmanship: We get the details right, from the words on the page to the system architecture. We have good taste. When we notice something isn’t right, we take the time to fix it. We are proud of the products we produce. We continuously self-reflect to continuously self-improve. - Intensity: We know we don’t have the luxury of patience. We play to win. We care about our product being the best, and when it isn’t, we fix it. When we fail, we talk about it openly and without blame so we succeed the next time. - Family: We know that balance and intensity are compatible, and we model it in our actions and processes. We are the best technology company for parents. We support and respect each other and celebrate each other’s personal and professional achievements. WHAT WE OFFER We want our benefits to reflect our values and offer the following to full-time employees: - Flexible (unlimited) paid time off - Medical, dental, and vision benefits for you and your family - Life insurance and disability benefits - Retirement plan dependent on country of employment - Parental leave - Fertility and family building benefits through Carrot - Lunch, as well as delicious snacks and coffee to keep you energized  - Discretionary benefit stipend giving people the ability to spend where it matters most - Free alphorn lessons These benefits are further detailed in Sierra's policies, may vary by region, and are subject to change at any time, consistent with the terms of any applicable compensation or benefits plans. Eligible full-time employees can participate in Sierra's equity plans subject to the terms of the applicable plans and policies. BE YOU, WITH US We're working to bring the transformative power of AI to every organization in the world. To do so, it is important to us that the diversity of our employees represents the diversity of our customers. We believe that our work and culture are better when we encourage, support, and respect different skills and experiences represented within our team. We encourage you to apply even if your experience doesn't precisely match the job description. We strive to evaluate all applicants consistently without regard to race, color, religion, gender, national origin, age, disability, veteran status, pregnancy, gender expression or identity, sexual orientation, citizenship, or any other legally protected class.

Location: San Francisco, CA Workplace: OnSite ABOUT US Sierra is the leading platform for customer-facing AI agents, working with many of the world's biggest brands — including The GAP, Rocket Mortgage, SoFi, Sutter Health, and SoftBank — to transform how they serve customers and grow their businesses. We are primarily an in-person company based in San Francisco, with growing offices across North America, Europe, and Asia. We are guided by a set of values that are at the core of our actions and define our culture: Trust, Customer Obsession, Craftsmanship, Intensity, and a commitment to balancing Family along the way. These values are the foundation of our work, and we are committed to upholding them in everything we do. Our co-founders are Bret Taylor https://www.linkedin.com/in/brettaylor/ and Clay Bavor https://www.linkedin.com/in/claybavor/. Bret currently serves as Board Chair of OpenAI. Previously, he was co-CEO of Salesforce (which had acquired the company he founded, Quip) and CTO of Facebook. Bret was also one of Google's earliest product managers and co-creator of Google Maps. Before founding Sierra, Clay spent 18 years at Google, where he most recently led Google Labs. Earlier, he started and led Google’s AR/VR effort, Project Starline, and Google Lens. Before that, Clay led the product and design teams for Google Workspace. ABOUT THE ROLE Sierra’s AI agents depend on foundation models to reason and act in real time. The Inference team builds the systems that make those models fast, reliable, and efficient at scale. As a Software Engineer on Inference, you’ll help define Sierra’s inference architecture across both self-hosted models and third-party inference providers. You’ll work on the systems responsible for serving and routing inference, managing capacity and quota, and optimizing for latency, reliability, and cost. This is a systems-first role at the intersection of distributed infrastructure and AI. You don’t need to be an ML researcher—we’re looking for engineers who love complex systems problems and are excited to apply that expertise to one of the fastest-moving areas of AI infrastructure. WHAT YOU'LL DO - Partner with frontier labs and providers. At our scale, we rely on frontier labs, and inference providers to supply capacity, training and inference infrastructure. - Shape Sierra’s inference architecture. Design how inference traffic flows across models, infrastructure, and providers, including new serving and proxy layers as Sierra scales. - Build for low latency and high reliability. Develop systems for routing, failover, capacity management, and quota that keep inference performant and available across large-scale production workloads. - Build and operate self-hosted inference. Run models on GPU infrastructure, from building containers and operating inference engines to managing the underlying compute capacity. - Optimize inference performance. Work with the Applied Research team on techniques such as speculative decoding and serving-engine optimizations that improve latency, throughput, and cost. - Build across a hybrid inference stack. Work with both Sierra-managed infrastructure and leading inference platforms, making architectural decisions about where and how workloads should run. - Push the serving stack forward. Work closely with inference providers to tune engines and infrastructure for Sierra’s workloads. - Support the broader model lifecycle. Contribute to infrastructure that enables post-training while partnering closely with our Models and Agent Runtime teams. WHAT YOU'LL BRING - Deep systems thinking and strong distributed systems fundamentals. - Experience designing, building, and operating large-scale production systems. - Strong judgment around tradeoffs involving latency, reliability, capacity, and cost. - Experience taking ownership of complex infrastructure from architecture through production operation. - Excitement about applying systems expertise to AI infrastructure and learning quickly as the underlying technology evolves. EVEN BETTER - Experience with ML infrastructure, MLOps, or production inference systems. - Experience serving LLMs or other large models at scale. - Experience operating self-hosted inference and GPU infrastructure. - Familiarity with inference frameworks such as vLLM or SGLang. - Experience with post-training infrastructure or inference-performance optimization. OUR VALUES - Trust: We build trust with our customers with our accountability, empathy, quality, and responsiveness. We build trust in AI by making it more accessible, safe, and useful. We build trust with each other by showing up for each other professionally and personally, creating an environment that enables all of us to do our best work. - Customer Obsession: We deeply understand our customers’ business goals and relentlessly focus on driving outcomes, not just technical milestones. Everyone at the company knows and spends time with our customers. When our customer is having an issue, we drop everything and fix it. - Craftsmanship: We get the details right, from the words on the page to the system architecture. We have good taste. When we notice something isn’t right, we take the time to fix it. We are proud of the products we produce. We continuously self-reflect to continuously self-improve. - Intensity: We know we don’t have the luxury of patience. We play to win. We care about our product being the best, and when it isn’t, we fix it. When we fail, we talk about it openly and without blame so we succeed the next time. - Family: We know that balance and intensity are compatible, and we model it in our actions and processes. We are the best technology company for parents. We support and respect each other and celebrate each other’s personal and professional achievements. WHAT WE OFFER We want our benefits to reflect our values and offer the following to full-time employees: - Flexible (unlimited) paid time off - Medical, dental, and vision benefits for you and your family - Life insurance and disability benefits - Retirement plan dependent on country of employment - Parental leave - Fertility and family building benefits through Carrot - Lunch, as well as delicious snacks and coffee to keep you energized  - Discretionary benefit stipend giving people the ability to spend where it matters most - Free alphorn lessons These benefits are further detailed in Sierra's policies, may vary by region, and are subject to change at any time, consistent with the terms of any applicable compensation or benefits plans. Eligible full-time employees can participate in Sierra's equity plans subject to the terms of the applicable plans and policies. BE YOU, WITH US We're working to bring the transformative power of AI to every organization in the world. To do so, it is important to us that the diversity of our employees represents the diversity of our customers. We believe that our work and culture are better when we encourage, support, and respect different skills and experiences represented within our team. We encourage you to apply even if your experience doesn't precisely match the job description. We strive to evaluate all applicants consistently without regard to race, color, religion, gender, national origin, age, disability, veteran status, pregnancy, gender expression or identity, sexual orientation, citizenship, or any other legally protected class.

Location: San Francisco; Toronto; New York; Montreal; Seattle Workplace: Hybrid ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE We're looking for distributed systems engineers and product-minded generalists to build the distributed runtime that powers large-scale LLM inference on Baseten. Our inference platform empowers customers to deploy and operate cutting-edge models with industry-leading performance, scalability, and reliability. It also powers Model APIs, our hosted endpoints for the latest open-source models. You'll work across the stack, from the developer experience customers use to deploy models, through the libraries behind features like tool calling and reasoning, down to the systems that orchestrate deployments on Kubernetes and route traffic efficiently. Your job is to make sure every model on our platform is fast, reliable, and cost-efficient. You'll join a small, high-impact team at the intersection of distributed systems, model performance, infrastructure, and product, helping define how developers use AI models at scale. This role is ideal for engineers who enjoy owning systems in production, solving hard integration problems, and making complex infrastructure simple and reliable for users.   EXAMPLE INITIATIVES You'll get to work on these types of projects on our Inference Platform: - The Baseten Inference Stack at NVIDIA Dynamo Day https://www.baseten.co/blog/nvidia-dynamo-day-baseten-inference-stack/ - 2x faster inference with KV cache-aware routing https://www.baseten.co/blog/how-baseten-achieved-2x-faster-inference-with-nvidia-dynamo/ - How Baseten multi-cloud capacity management (MCM) unifies deployments https://www.baseten.co/blog/how-baseten-multi-cloud-capacity-management-mcm-powers-cloud-self-hosted-and-hybr/#comparing-deployment-options-cloud-vs-self-hosted-vs-hybrid RESPONSIBILITIES - Build the infrastructure and orchestration systems that deploy and run large-scale distributed LLM inference, including routing, autoscaling, scheduling, and runtime management. - Design, build, and operate Model APIs, with a focus on advanced inference capabilities: structured outputs (JSON mode, grammar-constrained generation), tool/function calling, and multimodal serving. - Implement platform fundamentals such as API versioning, validation, usage metering, quotas, and authentication. - Instrument deep observability (metrics, traces, logs) and build repeatable benchmarks for speed, reliability, and quality. Help set best practices for testing, release automation, and operational excellence. - Debug and harden complex production systems spanning Kubernetes, distributed runtimes, networking, and GPU workloads to improve reliability and scalability. - Partner with Inference Performance engineers and other teams to make new optimizations broadly available to customers and easy to configure. - Own projects end to end, from architecture through deployment, monitoring, and iteration on customer feedback. Along the way, make thoughtful tradeoffs between performance, reliability, operational simplicity, and developer experience.   REQUIREMENTS - Bachelor's, Master's, or Ph.D. in Computer Science, Engineering, or a related field, or equivalent practical experience. - 3+ years building and operating distributed systems, backend infrastructure, or large-scale APIs where reliability, latency, and scale are first-class concerns. - A proven track record of owning low-latency, reliable backend services, including rate limiting, auth, quotas, metering, and migrations. - Infrastructure instincts with a feel for performance: profiling, tracing, capacity planning, and SLO management. - Comfort debugging performance and reliability issues across multiple layers of the stack, from application behavior down to runtime and infrastructure internals. - A strong sense of developer experience. You think about how systems are used, not just how they work. - Eagerness to learn new languages, frameworks, and systems, and a real interest in inference engineering. Prior ML or LLM experience isn't required, though experience with model serving or inference systems is a plus. - Excellent written communication and collaboration skills, including writing clear design docs and working across functions.   NICE TO HAVE - Experience with or contributions to LLM inference engines and frameworks such as vLLM, SGLang, TensorRT-LLM, TGI, or Dynamo. - Deep Kubernetes experience, including operators and custom resources, plus familiarity with service meshes or API gateways. - Experience with distributed scheduling, autoscaling, or service orchestration. - Experience operating GPU workloads in production. - A background in developer-facing infrastructure or APIs, or contributions to open-source infrastructure or ML systems. - Familiarity with observability tooling, CI/CD systems, or release automation. BENEFITS - Competitive compensation, including meaningful equity - (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents - Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) - Paid parental leave - Fertility and family-building stipend through Carrot - (U.S. only) Company-facilitated 401(k) - Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).

Location: San Francisco; Toronto; New York; Montreal; Seattle Workplace: Hybrid ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE We're looking for inference performance engineers who want to make the world's most demanding AI workloads run faster and more efficiently. You'll work across the stack, from the inference engine and runtime through scheduling, serving, and routing. Along the way you'll apply techniques like prefill/decode disaggregation, speculative decoding, and KV-cache management. You'll reason from first principles about where time and memory go, find what's holding performance back, and close the gap. Your work directly impacts how fast our customers' models run and how efficiently we serve them. This role is ideal for someone who thrives in a fast-paced startup environment and is eager to make significant contributions to the exciting field of LLM inference.   EXAMPLE INITIATIVES You'll get to work on these types of projects as an Inference Performance engineer: - Agentic Kernels in Production https://www.baseten.co/blog/agentic-kernels-in-production/ - How we built the new fastest API for GLM-5.2 https://www.baseten.co/blog/how-we-built-the-new-fastest-api-for-glm-52/ - Live draft model training for speculative decoding https://www.baseten.co/blog/live-draft-model-training-for-speculative-decoding/#eliminating-traditional-bottlenecks - Making Kimi K3 Tokenization 18x faster https://www.baseten.co/blog/making-kimi-k3-tokenization-18x-faster-for-million-token-agentic-workloads/ - The Baseten Inference Stack https://www.baseten.co/resources/guide/the-baseten-inference-stack/ - Driving model performance optimization https://www.baseten.co/blog/driving-model-performance-optimization-2024-highlights/ RESPONSIBILITIES - Implement and productionize cutting-edge inference techniques, working deep in runtime internals. That includes quantization, speculative decoding, KV-cache reuse, chunked prefill, LoRA, guided generation for structured outputs, and custom scheduling and routing algorithms. - Profile and optimize inference end to end, from kernel launch overhead and memory layout up to request scheduling, prefill/decode disaggregation, and cache-aware routing. Run cross-layer investigations, such as tracing a tail-latency regression from request timing through routing and batching down to a kernel. - Turn performance into cost savings. Improve tokens per GPU-hour, raise utilization, and give customers and internal teams clear latency/throughput/cost tradeoffs. - Bring up and tune new model architectures on new hardware quickly, often in the same week they're released. - Build benchmarking frameworks that measure real-world performance across model architectures, batch sizes, sequence lengths, and hardware configurations. - Contribute upstream to open-source inference engines (vLLM, SGLang, TensorRT-LLM), and partner closely with model, infrastructure, and customer-facing teams to ship wins.   REQUIREMENTS - Bachelor's, Master's, or Ph.D. degree in Computer Science, Engineering, Mathematics, or related field. - Experience with one or more general-purpose programming languages, such as Python or C++. - Familiarity with LLM optimization techniques (e.g., quantization, speculative decoding, continuous batching). - Strong familiarity with ML libraries, especially PyTorch, TensorRT, or TensorRT-LLM. - Demonstrated interest and experience in LLMs. - Deep understanding of GPU architecture.   NICE TO HAVE - Proficiency in enhancing the performance of software systems, particularly in the context of large language models (LLMs) - Contributed to vLLM, SGLang, TensorRT-LLM, or another inference engine. - Worked on large-scale distributed serving: autoscaling, load balancing, multi-region or multi-cloud capacity. - Written or optimized GPU kernels (CUDA, Triton, CUTLASS, or similar) - Worked on quantization (FP8/FP4, AWQ, GPTQ) or speculative decoding in production. - Deep understanding of software engineering principles and a proven track record of developing and deploying AI/ML inference solutions. BENEFITS - Competitive compensation, including meaningful equity - (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents - Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!) - Paid parental leave - Fertility and family-building stipend through Carrot - (U.S. only) Company-facilitated 401(k) - Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities. Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you. At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status. We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).

Location: Paris Workplace: OnSite ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. THE MISSION You will be the reference point for the most complex hardware interventions across our sites. Mistral's compute is our largest investment and the foundation of frontier AI in Europe. As a Senior Data Center Hardware Engineer, you own the interventions that require the highest level of technical expertise: multi-node failures in production, full-rack deployments of GPU-dense systems, and vendor escalations where you speak as the technical authority for our sites. You'll work end-to-end — compute, storage, interconnect, power and cooling interfaces — across our data center footprint, raising the bar on reliability as our capacity grows. This is a hands-on senior IC role: deep technical leadership and mentorship, no direct line management. Location: Based at one of our data centers in the Paris region, with occasional visits to our other sites as needed.   WHAT YOU'LL DO - Lead complex interventions — vendor-level and multi-node operations in production: full rack builds and bring-up, GPU-dense platform deployment (H100/H200-class and next-gen, including liquid-cooled racks), intricate recabling, post-restart diagnosis. You own the plan, the risk assessment, and the rollback. - Own vendor management — coordinate repairs, part replacements and escalations directly with hardware vendors; hold them to their SLAs; drive proactive fixes from failure trends. - Diagnose at depth — correlate symptoms across compute, storage, interconnect and cooling; read BMC/IPMI consoles, OS logs and crash dumps; methodical root-cause analysis that ends in concrete corrective actions. - Deploy and validate — structured bring-up and validation of new racks and platforms; work with networking and infrastructure teams to take installations from delivery to production-ready. - Lift the team — mentor and pair with L1–L3 engineers on live operations; turn your knowledge into SOPs, checklists and training material; propose and build small automation (Python/Bash) to shorten MTTR. - Work the facility interface — partner with critical facilities/engineering ops on power, cooling (air and liquid), PDU and monitoring (BMS/EPMS) boundaries; enforce ESD/LOTO/PPE and audit-ready change discipline everywhere. - Own spares & reliability — plan spares strategy, track fleet failure trends, and run proactive vendor actions before incidents happen.   ABOUT YOU - 6+ years in data center / server hardware or L2/L3 hardware support, including complex hands-on work in production environments (HPC/AI/cloud at scale strongly preferred). - End-to-end hardware expertise: CPU/memory/PCIe (incl. accelerators), NICs, PSUs, drives, network, power and cooling interfaces (DLC included); strong judgment on when and how to escalate. - Diagnostics depth: confident with BMC/IPMI logs, Linux logs and CLI checks; structured root-cause analysis. - Deployment experience: you've taken racks from delivery to production, with concrete examples to show for it. - Vendor fluency: you've managed hardware vendors — RMAs, field replacements, escalations — to concrete outcomes. - Impeccable safety and discipline: ESD/LOTO/PPE without exception; clean, labeled, auditable work. - Communication & mentoring: clear status updates and handovers; able to coach engineers during live incidents with composure. - Mobility: willing to visit our other sites in the Paris region as needed.   NICE TO HAVE - GPU-dense platform experience: NVIDIA H-series (H100/H200) or GB-series rack deployments. - High-speed interconnects (InfiniBand/RoCE), RAID/storage (NVMe/SAS/SATA), vendor tooling (iDRAC/iLO/IPMI). - Automation (Python/Bash) for ops tooling and reporting; ticketing (Jira/ServiceNow), inventory/RMA flows. - Hardware qualification, burn-in or factory validation background. WHAT WE OFFER We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link. PRIVACY POLICY Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

Location: San Francisco, CA | New York City, NY | 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 Frontier AI is on track to be among the most consequential and most adversarially-targeted technology in the world. The capability curve is steep, the adversaries who want these systems are extremely well-resourced, and the security bar this will eventually require is well beyond where the industry operates today. Incremental hardening alone is not going to close that gap, so we need breakthroughs and a group of people to go find them. Security Labs is that team. We run a portfolio of high-risk, high-expected-value security projects: the work that seems impractical until someone optimistic and stubborn enough actually tries it. Projects run on the order of weeks rather than quarters, and each one is either handed off to the Anthropic team that will own it in production or wound down with a writeup of what we learned. We expect a meaningful fraction of our bets not to land. This team is intentionally scrappy and ambitious. We're intentionally iterating fast on high-risk research bets and we expect a meaningful fraction not to land. If a 30% project success rate sounds uncomfortable, or uncharted and chaotic territory isn’t both frightening and exciting, this probably isn't the right fit. There are other places in Anthropic Security doing important work with more structure, less risk, and more certain paths to positive outcomes. The questions we're trying to answer include: Can our core research workflows survive extreme isolation? Can we replace trust with cryptographic guarantees? Can the models themselves become our most effective security control? What would it actually take to defend against a tier-1 state adversary, and how much of that can we build now? Who we're looking for. We're hiring generalists with rare depth. You're a strong software engineer as a baseline, and on top of that you've gone deep in at least one area most engineers don't get near: firmware or hardware security, applied cryptography, OS / kernel / hypervisor internals, formal methods, reverse engineering, or high-assurance and cross-domain systems. You've built things under your own direction, you're comfortable jumping layers when the problem demands it, and you'd rather take a swing at something that might not work than ship the safe incremental thing. You think the trajectory of AI matters a great deal, you're not comfortable with how the security side of it is going by default, and you'd rather be on the inside building the answer than watching from outside. Current Project Areas The portfolio changes as we learn. The kinds of bets currently in flight or queued: Standing up a prototype high-assurance research cluster: running real Anthropic training and research workloads under extreme isolation and physical security controls, and finding out exactly where productivity breaks and what we'd need to invent to get it back Provable inference: cryptographic verification (zero-knowledge proofs, attestation chains) that a given output came from a specific model running specific code, replacing "trust us" with math Build hypervisor-isolated microVM workload isolation across the fleet Compiling an ML kernel through a formally verified pipeline where every lowering step carries a machine-checked proof of equivalence, making compilation-layer sabotage mathematically detectable Regenerating clusters: automation that spins up a purpose-built cell, runs a workload, and tears the whole thing down on a TTL measured in hours, so attacker persistence has an expiry date Using Claude itself to drive security work end to end: threat modeling new compute platforms, rewriting critical services to zero external dependencies, running the test equipment that validates what hardware datasheets claim Part of your job is deciding what comes next. We hire people we trust to pick good bets, and project selection is owned by the engineers doing the work. Key responsibilities Own Security Labs projects end to end. You'll scope the bet, build the prototype, run it against real workloads, and bring it to either a hand-off or a documented exit Stand up novel security infrastructure fast (isolated clusters, attestation chains, hypervisor and runtime work, verification tooling) optimizing for what we learn rather than for permanence Find the receiving team early, bring them along while you build, and hand them something they actually want to own Work embedded with research and infrastructure teams (Pretraining, RL, Inference, Compute) to test whether their workflows survive what you're proposing, and document precisely where they don't Turn experimental results into short writeups that shape Anthropic's long-term security architecture, and into costed contingency plans we could execute on short notice Help pick the next round of bets and influence the industry to get better along the way Minimum qualifications Genuinely care about where AI is heading and want to work on the security problems that determine whether it goes well. This is the most important thing on this list Have real depth in at least one area most software engineers don't touch (e.g. firmware or hardware security, applied cryptography, OS / kernel / hypervisor internals, formal methods and verification, reverse engineering and exploit development, or high-assurance / cross-domain systems) Have built and shipped things under your own direction. Maybe you founded a company or research group, maintained an open-source project other people depend on, or shipped research that changed how people in your field work. We weight this far more than where you've worked or for how long Have a track record of choosing the problem yourself and seeing it through, rather than only executing a plan someone else handed you Are comfortable jumping between domains and layers of the stack when the problem calls for it, and have actually done so Have run prototypes or experiments where the goal was answering a hard question rather than shipping a permanent system, including ones that didn't pan out Write clearly enough to turn weeks of work into a couple of pages someone can act on Change your mind when the evidence says to, and are fine being the least-expert person in a room full of specialists Care about defense. Plenty of folks here come from offense and that background is valuable, but what you actually want to spend your time on now is making systems hold up Are a strong programmer (Python plus at least one of Rust, Go, or C/C++) and can stand up real infrastructure without that being the interesting part of your week Preferred qualifications Experience inside airgapped or high-side environments (classified networks, cross-domain solutions, ICS/SCADA, financial trading infrastructure) and the operational realities of working in them A background in offensive security, red teaming, or vulnerability research, with calibrated intuitions for which threats actually matter Familiarity with ML infrastructure (training pipelines, distributed schedulers, inference serving, accelerator hardware) sufficient for grounded conversations with researchers about what their workloads actually need A history of working in environments built around rapid iteration rather than rigid change control: startups, applied research groups, independent consulting, small security shops 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. 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: Minnesota, United States About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role We are seeking a Critical Infrastructure Engineer to help ensure the availability, resilience, and operational readiness of the critical systems supporting Nebius data center IT infrastructure. The primary objective of this role is uptime . You will provide technical oversight across the electrical and mechanical infrastructure responsible for delivering reliable power and cooling to our GPU and IT environments. Rather than serving primarily as a maintenance technician, you will verify that critical infrastructure is operated safely, consistently, and in accordance with established SLAs, engineering standards, change-control procedures, and operational best practices. You will also act as an important bridge between IT infrastructure teams and electrical/mechanical specialists. The ideal candidate understands how servers, networking equipment, racks, and GPU systems operate inside a data center while also having enough exposure to critical facilities systems to understand—and challenge when necessary—the infrastructure supporting them. The position combines technical analysis, provider governance, change management, incident response, and hands-on familiarity with data center IT environments. You will work onsite in our Minnesota data center location. Your responsibilities will include: Critical Infrastructure & Uptime Help ensure the availability and operational readiness of the electrical and mechanical infrastructure supporting production data halls and high-density GPU environments. Monitor critical infrastructure performance against contractual SLAs, operational requirements, and established reliability standards. Develop a strong understanding of the complete power and cooling path supporting IT equipment and identify conditions that could introduce operational risk. Review infrastructure capacity, redundancy, and operating conditions to ensure the environment can reliably support current and planned compute deployments. Identify infrastructure risks and work with service providers and internal teams to drive corrective actions before they impact production. Support infrastructure planning for data center expansions, capacity increases, and new GPU deployments. Power & Electrical Infrastructure Provide technical oversight of data center electrical infrastructure, including generator plants, automatic transfer switches (ATS), UPS systems, battery banks, switchgear, breakers, busbars, bus plugs, PDUs, and related power distribution equipment. Understand electrical distribution from facility-level infrastructure through rack-level delivery and IT equipment. Participate in technical reviews involving power capacity, electrical distribution, equipment sizing, redundancy, and infrastructure design. Work with electrical engineers and infrastructure providers to evaluate proposed changes and ensure appropriate engineering validation is completed before production implementation. Cooling & Mechanical Infrastructure Understand the cooling architecture supporting high-density GPU and IT environments, including water and glycol loops, rear-door heat exchangers (RDHx), evaporative systems, coolant distribution systems, facility water systems, dry coolers, and chillers. Evaluate how cooling infrastructure interacts with GPU systems and high-density racks to maintain required operating conditions. Partner with mechanical engineers and service providers to review system performance, capacity constraints, and proposed infrastructure changes. Identify potential thermal or cooling risks that could affect compute availability or future capacity. Provider Governance & Change Control Provide technical oversight of third-party critical infrastructure and colocation service providers. Ensure provider activities comply with Nebius policies, approved procedures, contractual SLAs, and operational requirements. Review and approve change requests involving critical infrastructure supporting production environments. Challenge incomplete or high-risk work plans and ensure appropriate testing, rollback procedures, risk analysis, and stakeholder communication are in place before work begins. Maintain strong governance around maintenance and infrastructure changes that could affect production availability. Hold service providers accountable for corrective actions, operational performance, and agreed service levels. Incident Response & Operational Risk Participate in critical infrastructure incidents and coordinate technical response with providers, Data Center IT, networking, and engineering teams. Support root-cause analysis following power, cooling, or infrastructure-related incidents. Review incident findings and ensure corrective and preventive actions are documented, assigned, and completed. Help develop and continuously improve emergency response procedures, escalation paths, change-control standards, and operational documentation. Identify recurring infrastructure risks and drive improvements that increase reliability and reduce the likelihood of customer impact. IT & Critical Infrastructure Integration Work closely with Data Center IT teams to understand how critical infrastructure conditions affect servers, networking equipment, GPU clusters, and other production systems. Apply practical knowledge of data center IT operations, including racks, servers, fiber, cabling, network equipment, and hardware deployment. Support cross-functional troubleshooting where the root cause may span IT equipment and facility infrastructure. Help create stronger operational alignment between IT infrastructure and electrical/mechanical teams. Reporting & Stakeholder Communication Translate complex infrastructure conditions, incidents, risks, and provider performance into clear information for technical and business leadership. Develop reports, dashboards, presentations, and operational analyses related to uptime, infrastructure performance, capacity, incidents, and service-provider performance. Participate in technical and leadership meetings as a subject-matter resource for data center critical infrastructure. Use operational data to identify trends, communicate risk, and drive measurable improvements in reliability and provider performance. We expect you to have: Experience working in data center, cloud infrastructure, colocation, critical facilities, or other mission-critical environments. Practical understanding of IT infrastructure, including servers, racks, networking equipment, structured cabling, and fiber. Working knowledge of data center electrical infrastructure such as UPS systems, generators, switchgear, PDUs, batteries, breakers, and power distribution. Exposure to data center mechanical and cooling systems, including chilled-water, glycol, liquid-cooling, or comparable thermal-management environments. Ability to understand how electrical and mechanical infrastructure directly impacts IT equipment availability and performance. Experience participating in infrastructure change management, incident response, operational risk management, or maintenance governance. Ability to review technical plans, ask detailed engineering questions, identify risk, and work effectively with electrical and mechanical subject-matter experts. Strong analytical skills with experience using Excel for reporting, data analysis, and operational metrics. Strong written and verbal communication skills with the ability to communicate effectively with engineers, vendors, service providers, and senior leadership. A proactive, ownership-driven approach with the ability to operate effectively in a high-availability production environment. Nice to have: Experience supporting high-density GPU, AI, HPC, or hyperscale data center environments. Experience with direct-to-chip liquid cooling or other advanced cooling technologies used for high-density compute. Experience managing colocation or third-party critical infrastructure providers against contractual SLAs. Familiarity with Tier III data center environments and high-availability infrastructure principles. Experience developing or implementing change-management, incident-response, or emergency-response procedures. Experience supporting infrastructure capacity planning, expansion projects, or new data center deployments. Relevant electrical, mechanical, data center, or critical facilities certifications. Key Employee Benefits in the US: Health Insurance: 100% company-paid medical, dental, and vision coverage for employees and families. 401(k) Plan: Up to 4% company match with immediate vesting. Parental Leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers. Disability & Life Insurance: Company-paid short-term, long-term, and life insurance coverage. Pay Transparency We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law. Base Compensation Range $45 — $57 USD Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Location: Boston, MA Workplace: OnSite At WHOOP, we're on a mission to unlock and inspire performance for life. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives. By combining advanced sensing, data science, and technology designed for continuous wear, WHOOP delivers insights that help members better understand and improve their health and performance. WHOOP is looking for a Senior Sensor Intelligence Engineer to join our Embedded Controls team within Sensor Intelligence and help shape the sensing capabilities across current and future WHOOP devices. Sitting at the intersection of signal processing, machine learning, embedded systems, and sensor technology, you will develop algorithms that interpret real-world sensor signals and intelligently control how sensors operate on-device. You will work across the sensing stack from understanding sensor physics and signal characteristics through algorithm development and embedded deployment to enable increasingly sophisticated sensing capabilities while meeting the power, compute, memory, and reliability constraints of a continuously worn device. RESPONSIBILITIES: - Develop signal-processing and machine-learning algorithms that interpret physiological, motion, and other wearable sensor signals, including applications such as activity and state detection, wear detection, signal-quality assessment, and device-state estimation - Design intelligent embedded control strategies that dynamically configure sensor behavior based on signal quality, device state, user context, system requirements, and power constraints - Characterize new and existing sensors by understanding signal behavior, noise sources, artifacts, dynamic range, sampling requirements, calibration, operating modes, and the impact of analog and acquisition architectures - Prototype algorithms and sensing strategies using tools such as Python or MATLAB, then partner with Firmware Engineering to translate them into robust, computationally efficient implementations suitable for resource-constrained embedded systems - Partner closely with Firmware, Electrical Engineering, Data Science, Hardware, and broader Sensor Intelligence teams to bring up new sensing modalities, debug issues across the sensing stack, and inform sensing architectures for future WHOOP products - Develop experiments, analysis frameworks, and validation methodologies to evaluate sensor and algorithm performance across users and real-world conditions, optimizing solutions for accuracy, robustness, latency, memory, compute, and power QUALIFICATIONS: - BS, MS, or PhD in Electrical Engineering, Computer Engineering, Biomedical Engineering, Computer Science, Applied Physics, or a related technical field, or equivalent practical experience - Strong foundation in digital signal processing and time-series analysis, with experience applying techniques such as filtering, spectral analysis, sampling theory, noise reduction, and feature extraction to real-world sensor data - Experience developing signal-processing and/or machine-learning algorithms for noisy, artifact-prone sensor data and taking algorithms beyond offline analysis toward real-time, embedded, or production implementation - Strong proficiency in Python, MATLAB, or similar algorithm-development environments, with working knowledge of C/C++ and embedded-system considerations such as timing, memory, compute, and hardware interfaces - Working knowledge of sensor and electrical systems, including concepts such as ADCs, analog front ends, sampling, digital interfaces, noise, calibration, and signal acquisition - Ability to investigate and solve ambiguous engineering problems across algorithm, firmware, sensor, and electrical boundaries using strong experimental and analytical methods - Experience with wearable, physiological, optical, impedance, motion, multimodal, or related sensor systems is valued; experience with embedded inference, sensor fusion, fixed-point processing, quantization, low-power sensing, or microcontroller deployment is a plus - Strong commitment to embracing and leveraging AI tools in day-to-day tasks, ensuring AI-assisted work aligns with the same high-quality standards as personal contributions. Interested in the role, but don't meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply. WHOOP is an Equal Opportunity Employer and participates in E-Verify to determine employment eligibility. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.