Вакансии

Location: Paris; San Francisco 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. Mistral AI is building a new Compute Support team to ensure the reliability, performance, and scalability of our GPU clusters in a Kubernetes environment. As one of the founding members of this team, you will play a pivotal role in shaping its processes, standards, and culture. In this hybrid L1/L2 support role, you will be the first point of contact for customers and internal teams, providing technical guidance, troubleshooting, and issue resolution for compute-related inquiries. You’ll also serve as the escalation point for complex issues, leveraging your deep systems knowledge, Kubernetes expertise, and operational debugging skills to ensure our AI workloads run smoothly at scale. This is a support-focused role that blends system administration, customer-facing communication, and compute infrastructure expertise. While coding is not the primary focus, familiarity with Go for debugging and automation is a plus.     KEY RESPONSIBILITIES FIRST CUSTOMER’S POINT OF CONTACT - Act as the first interlocutor for customers and internal teams, providing timely and effective responses to compute-related inquiries. - Triage and prioritize incoming requests, ensuring SLAs are met for acknowledgment, first response, and resolution. - Gather and analyze initial issue details (e.g., logs, error messages, system metrics) to diagnose problems efficiently. - Provide clear, actionable guidance to customers and internal users, including temporary workarounds where applicable.     TECHNICAL SUPPORT - Serve as the L2 escalation point for Linux, Kubernetes, and compute infrastructure issues, providing deep technical troubleshooting for: - GPU/TPU workloads (e.g., CUDA errors, memory leaks, job failures). - Kubernetes clusters (e.g., pod crashes, node failures, networking misconfigurations). - Bare metal and cloud environments (e.g., AWS EC2, GCP VMs, HPC clusters). - Diagnose and resolve performance bottlenecks, hardware failures, and resource contention in distributed systems. - Analyze system metrics, logs, and traces (e.g., dmesg, journalctl, nvidia-smi, Prometheus, Grafana) to identify root causes of issues. - Participate in on-call rotations to provide 24/7 support for critical compute systems - Nice to have: Optimize system configurations (e.g., kernel parameters, filesystem tuning, network settings) for high-performance computing (HPC) and AI workloads.     KUBERNETES & CONTAINERIZATION - Debug Kubernetes clusters with a focus on: - Pod and node issues (e.g., CrashLoopBackOff, OOMKilled, ImagePullBackOff). - Networking and storage (e.g., CNI plugins, PersistentVolumes, StorageClasses). - Resource management (e.g., Requests/Limits, QOS classes, node affinity). - Troubleshoot container runtime issues (Docker, containerd) such as image pull failures, OCI compliance, or runtime errors. - Collaborate with SRE teams to understand and improve existing IaC (Terraform, Ansible) and Go-based tooling.     DOCUMENTATION & PROCESS IMPROVEMENT - Create and maintain runbooks, playbooks, and internal documentation for common compute issues (e.g., GPU debugging, Kubernetes troubleshooting). - Contribute to post-mortems with actionable follow-ups to prevent recurring incidents. - Train internal teams on best practices for compute infrastructure and debugging. - Help define and refine support processes as a founding member of the new Compute Support team. What We’re Looking For REQUIRED SKILLS & EXPERIENCE - 5+ years of experience in system administration, technical support, or infrastructure operations, with a strong focus on compute-heavy environments (bare metal, cloud, HPC, or virtualization). - Deep Linux/Unix expertise: - Debugging kernel-level issues (e.g., OOM killer, I/O bottlenecks, CPU throttling). - Performance tuning (e.g., sysctl, ulimit, filesystem optimizations). - Networking troubleshooting (e.g., iptables, tcpdump, DNS, NFS, SSH). - Storage management (e.g., LVM, RAID, NVMe, GPU-local storage). - Hands-on Kubernetes experience is mandatory: - Debugging pods, nodes, and clusters (e.g., kubectl describe, kubectl logs, crictl). - Networking and storage (e.g., CNI plugins, PersistentVolumes). - Resource management (e.g., Requests/Limits, QOS classes). - Familiarity with containerization (Docker, containerd) and basic IaC awareness Proficiency with monitoring tools (Prometheus, Grafana, ELK, OpenTelemetry). - Basic scripting/automation skills (preferably Go, but Bash or Python are acceptable for ad-hoc tasks). - Experience with bare metal, virtualization, or HPC environments (e.g., Fluidstack, Coreweave, Vast) is a strong plus. - Excellent problem-solving and communication skills, with the ability to explain technical issues clearly and patiently to both technical and non-technical stakeholders. - Customer-focused mindset with a passion for resolving issues efficiently and improving system reliability. - Ability to work in on-call rotations and handle high-pressure situations with a structured, calm approach. - Commitment to meeting tight SLAs for issue acknowledgment, triage, and first response. 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.

Задачи: — Оценить текущее состояние технического отдела. — Понять, что стоит доработать, какими силами и в каком порядке. — Навести инженерную гигиену. — Выстроить процессы, которых сейчас нет: код-ревью, тесты, CI/CD, стандарты. — Взять на себя архитектуру и безопасность — закрыть периметр, навести порядок с секретами и доступами. — Развивать AI-пайплайн: интеграции с моделями по API, очереди генерации, отказоустойчивость, контроль стоимости. — Собрать команду под себя (штат + аутсорс) вместе с HR — нанимать, вести, контролировать качество. Требования: — За плечами 5+ лет на C# / .NET, уверенно с EF Core, PostgreSQL, Docker. — Есть роль техлида/архитектора/CTO в продуктовой команде и опыт сборки команды. — Реальный код-ревью, тесты, безопасность (JWT/OAuth, OWASP). — Опыт интеграций по API и проектирования пайплайнов; работа с ИИ по API — плюс. — Работал с Claude Code / AI-ассистентами разработки — умеешь использовать инструменты для повышения эффективности. Условия: — Зарплата: реальная сумма на руки — от 300 000 до 500 000 ₽. — Формат: полная удалёнка (СНГ / РФ), график гибкий, в рабочем окне (обсуждаемо). — Оформление: договор / ИП / самозанятость — как удобно. — После испытательного периода обсуждаем опцион.

Лаборатория «Космос» — российский стартап, создающий собственные программно-аппаратные продукты: от первых прототипов до готовых устройств. В портфеле два продукта: «Каспер» — робо-компьютер для обучения детей; «Света» — интерактивная светозвуковая панель для дома и различных пространств. Задачи: — исследовать новые AI-модели, сервисы и способы их применения; — решать отдельные продуктовые задачи с помощью существующих AI-технологий: исследовать рынок, выбирать решение, тестировать и собирать прототип, например, найти и интегрировать технологию для работы Каспера с произношением ребёнка; — собирать внутренние AI-инструменты для команды из готовых моделей, API и автоматизаций; — автоматизировать работу с документами, голосовыми сообщениями, ссылками, PDF и базой знаний; — создавать инструменты для работы команды с видео, изображением, музыкой, звуком и другим контентом; — проводить исследования по продуктам, технологиям, конкурентам и патентам. Требования: — очень хорошее знание современных нейросетей, основных моделей и сервисов; — постоянное отслеживание новинок и тестирование; — умение применять AI-технологии для решения задач; — API, автоматизации и базовый код важны; — опыт классической разработки будет плюсом, но не обязателен; — самостоятельность, любопытство, способность быстро проходить путь от задачи и исследования до работающего решения. Условия: — работа в офисе в Санкт-Петербурге, центр города, м. Горьковская; — оплачиваемая работа; — занятость и график обсуждаются индивидуально; — уровень вознаграждения зависит от навыков, опыта и объёма занятости.

Location: Singapore 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 give you the opportunity to work with cutting-edge technologies in data operations, cloud computing and infrastructure management. As global data center operations grow, there will be ample opportunities for career progression. Working in the data center directly impacts performance, customer satisfaction and efficiency, with the opportunity to contribute to new data center projects.You’ll collaborate with experts in AI data center development and operations, gaining insights from leaders in the field. This environment fosters innovation, and allows you to work on solutions that exceed industry standards in design and deployment. As a Field Delivery Engineer, you will be leading the on-site delivery and deployment of hardware in new locations.You will plan, coordinate, and supervise activities from rack acceptance tests through full operational readiness, ensuring that servers, storage, networking, and supporting systems are installed and functioning according to specifications. Your responsibilities will include: Planning & Coordination Own the hardware deployment plan for new sites, aligning timelines, scope, and resources with project stakeholders. Coordinate with logistics, supply chain, data center construction, and network teams to prepare GPU servers for delivery to customers. Verify all pre-deployment requirements (racks, power, cooling, connectivity) are met. On-Site Delivery & Installation Supervise racking, cabling, and powering of hardware equipment. Ensure configuration, firmware upgrades, and integration with management systems follow standards. Work closely with vendors, contractors, and local staff during the installation phase. Quality & Readiness Conduct functional checks, acceptance tests, and troubleshooting to confirm all systems operate correctly. Document installation steps, asset data, and lessons learned for future rollouts. Establish and maintain best practices for safe, efficient hardware deployment. Collaboration & Reporting Act as the single point of contact on site for deployment activities, providing clear updates to project managers and leadership. Support transition of deployed hardware to operations teams for steady-state management. Contribute feedback to improve hardware design and deployment playbooks. We expect you to have: 3+ years' experience in data center, hardware, or IT field delivery roles. Strong knowledge of server, storage, and network installation practices (racking, cabling, power, cooling). Strong hands-on experience working on datacenter floors with server hardware and Linux troubleshooting. Proven track record coordinating deployments across multiple stakeholders and vendors. Ability to read and interpret technical documentation, layouts, and wiring diagrams. Ability to perform deep diagnostics across hardware, firmware (BIOS/ BMC ), and Linux (logs, drivers, storage stack basics, performance triage). Excellent organizational, communication, and problem-solving skills. Willingness to travel frequently (sometimes at short notice). It will be an added bonus if you have: Strong familiarity with GPU server platforms and tooling (for example: nvidia-smi, dcgmi, GPU and system logs correlation). Experience with Redfish and BMC tooling (ipmitool/Redfish), firmware lifecycle, and staged rollouts. Scripting skills (bash and basic Python) for log collection, triage automation, and simple reliability analysis. Exposure to OCP -based platforms and ODM manufacturing ecosystems. Familiarity with change management, asset tracking, and quality-assurance processes. Understanding of health, safety, and environmental standards in datacenter or industrial settings. 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: Hyderabad, India 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 give you the opportunity to work with cutting-edge technologies in data operations, cloud computing and infrastructure management. As global data center operations grow, there will be ample opportunities for career progression. Working in the data center directly impacts performance, customer satisfaction and efficiency, with the opportunity to contribute to new data center projects.You’ll collaborate with experts in AI data center development and operations, gaining insights from leaders in the field. This environment fosters innovation, and allows you to work on solutions that exceed industry standards in design and deployment. As a Field Delivery Engineer, you will be leading the on-site delivery and deployment of hardware in new locations.You will plan, coordinate, and supervise activities from rack acceptance tests through full operational readiness, ensuring that servers, storage, networking, and supporting systems are installed and functioning according to specifications. Your responsibilities will include: Planning & Coordination Own the hardware deployment plan for new sites, aligning timelines, scope, and resources with project stakeholders. Coordinate with logistics, supply chain, data center construction, and network teams to prepare GPU servers for delivery to customers. Verify all pre-deployment requirements (racks, power, cooling, connectivity) are met. On-Site Delivery & Installation Supervise racking, cabling, and powering of hardware equipment. Ensure configuration, firmware upgrades, and integration with management systems follow standards. Work closely with vendors, contractors, and local staff during the installation phase. Quality & Readiness Conduct functional checks, acceptance tests, and troubleshooting to confirm all systems operate correctly. Document installation steps, asset data, and lessons learned for future rollouts. Establish and maintain best practices for safe, efficient hardware deployment. Collaboration & Reporting Act as the single point of contact on site for deployment activities, providing clear updates to project managers and leadership. Support transition of deployed hardware to operations teams for steady-state management. Contribute feedback to improve hardware design and deployment playbooks. We expect you to have: 3+ years' experience in data center, hardware, or IT field delivery roles. Strong knowledge of server, storage, and network installation practices (racking, cabling, power, cooling). Strong hands-on experience working on datacenter floors with server hardware and Linux troubleshooting. Proven track record coordinating deployments across multiple stakeholders and vendors. Ability to read and interpret technical documentation, layouts, and wiring diagrams. Ability to perform deep diagnostics across hardware, firmware (BIOS/ BMC ), and Linux (logs, drivers, storage stack basics, performance triage). Excellent organizational, communication, and problem-solving skills. Willingness to travel frequently (sometimes at short notice). It will be an added bonus if you have: Strong familiarity with GPU server platforms and tooling (for example: nvidia-smi, dcgmi, GPU and system logs correlation). Experience with Redfish and BMC tooling (ipmitool/Redfish), firmware lifecycle, and staged rollouts. Scripting skills (bash and basic Python) for log collection, triage automation, and simple reliability analysis. Exposure to OCP -based platforms and ODM manufacturing ecosystems. Familiarity with change management, asset tracking, and quality-assurance processes. Understanding of health, safety, and environmental standards in datacenter or industrial settings. 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: Remote, Poland; Armenia; Agios Athanasios, Cyprus; Tbilisi, Georgia; Kazakhstan; Portugal; Bucharest, Romania; Serbia Workplace: fully Simple Life is the #1 AI-powered health coaching app for adults who want to lose weight and enjoy a healthier lifestyle—without the stress or extremes. Our mission is to empower people to feel their best every day. By challenging traditional, restrictive approaches, Simple offers a more sustainable method grounded in ease, personalization, and real-life support. Simple has had over 17 million downloads and more than 300,000 5-star reviews, having helped millions lose weight successfully and sustainably. Simple has earned recognition as Best Virtual Coach and one of the Top 100 AI companies — all thanks to a dedicated global team driving real impact. With SIMPLE as a partner in their pocket, users feel cared for and empowered to embrace — and stick to — new healthy habits. To learn more, visit simple.life . We are looking for an experienced IT Systems Engineer who will join our Internal IT team. Push the pace of innovation and build a future of a healthier world with us! What we can offer you to work on: Provide technical support for ~300 employees / contractors; Ensure security and reliability of approximately 300 Apple devices and 20–30 Windows / Android devices. Manage team members identity and access, including on / off boardings; Participate in SaaS tools procurement and inventory; Participate in hardware equipment procurement and inventory; Help us build and maintain the knowledge base of Internal IT domain; Address potential and actual security breaches, escalating and / or resolving upon discovery; Develop automated solutions as we grow (on / off boarding, etc); Help us shape the processes and form of internal IT; What we look for: 3+ years experience with MDM for macOS on 300+ devices; 3+ years experience managing Google Workspaces, Slack, Entra ID, Jira, Confluence, Cloudflare Zerotrust or similar services; Understanding of the identity and access management in a SaaS-heavy environments; Network basics: static routing, switching, core protocols, wireless; Experience working with infrastructure as code - Our VPN configuration is defined in Terraform; Experience programming with JavaScript / TypeScript / Python; Comfortable communicating with diverse groups of people across the company; At least 6 hours overlap with the office timezone (Cyprus); Beneficial skills: Deep macOS knowledge: hardening, CLI tools, specific setups (always on VPN and so on); Deep Windows knowledge: internals, enrollment methods, security hardening, strong troubleshooting skills; Experience with EDR solutions; Experience with RBI / VDI solutions; Experience with distributed logging / tracing; Experience with Grafana / Prometheus; Previous experience in dynamic product IT companies; Perks and benefits: Competitive salary package commensurate with experience; Open-minded teams, a welcoming and inclusive company culture, plus the opportunity to make a real difference with a game-changing health tech product; Flexible hours. We focus on your results, not how long you spend at your desk.

Требования: — Высшее образование (бакалавр или магистр) в области электронной инженерии с отличными академическими результатами — Опыт работы от 2 до 5 лет

Location: Москва **ИТ B2C** — самая крупная экосистема в Сбере. Нас более 8000 человек в 18 городах России. Мы занимаемся разработкой и развитием розничных решений, помогая сделать сервисы Банка доступнее, безопаснее и удобнее. Ждем именно тебя! Мы — команда экспертов, объединённых общей страстью к искусственному интеллекту и рекомендательным системам. Наша главная задача — создание современной масштабируемой рекомендательной платформы, способной предвосхищать ожидания пользователей и предлагать персонализированные рекомендации на каждом этапе их взаимодействия с экосистемой Сбер. Наши решения охватывают широкий спектр отраслей: от финансов и e-commerce до индустрии развлечений и здравоохранения. Мы следим за развитием технологий, экспериментируем с новыми ML-подходами, внедряем их в платформу и доводим до конкретного применения в бизнесе. Мы ищем **Lead ML Engineer**, который будет отвечать за технологическое развитие направления рекомендательных систем и формирование единой инженерной и операционной среды для DS- и MLE-команд. В зоне ответственности — скорость и воспроизводимость проверки DS-гипотез, качество разработки и вывода моделей в production, а также надёжная эксплуатация ML-решений с предсказуемыми качеством и стоимостью. Обязанности * формировать технологическое видение и roadmap развития ML Engineering и MLOps в направлении рекомендательных систем, определяя ключевые инженерные приоритеты и точки роста * развивать единый жизненный цикл ML-решений — от формулирования и воспроизводимой проверки DS-гипотез до запуска моделей в production, мониторинга и дальнейшего сопровождения * создавать инженерные стандарты и практики, которые помогают командам выпускать качественные и надёжные модели: требования к коду и данным, тестированию, версионированию, документированию, релизам * управлять портфелем технологических инициатив, находя баланс между развитием новых возможностей, повышением надёжности и устранением архитектурных ограничений * повышать производительность DS/MLE команд за счёт автоматизации, стандартизации, переиспользуемых компонентов и упрощения пути от эксперимента до production * развивать применение GenAI в процессе создания ML-решений: находить наиболее перспективные сценарии, запускать пилоты и оценивать их влияние на скорость и качество разработки * повышать масштабируемость, надёжность и экономическую эффективность ML-инфраструктуры * развивать ML-инженеров и технических лидеров направления. Требования * опыт технического лидерства в крупном ML-направлении не менее двух лет: умение формировать технологическое видение и roadmap, а также доводить системные изменения до результата * глубокое понимание полного жизненного цикла ML-решений — от эксперимента и обучения модели до production, мониторинга, масштабирования и дальнейшего сопровождения * опыт проектирования и развития высоконагруженных ML-систем, включая batch-, streaming- и real-time-сценарии * опыт взаимодействия с продуктовыми, платформенными и инфраструктурными командами, включая согласование приоритетов и защиту потребностей своего направления * умение принимать решения в условиях конфликтующих приоритетов, объяснять компромиссы и доводить выбранные инициативы до результата * опыт people management в DS/MLE командах: найм и адаптация сотрудников, постановка целей, регулярная обратная связь, performance review, развитие и удержание специалистов, а также формирование среды для обмена знаниями и распространения сильных инженерных практик * опыт внедрения GenAI-инструментов в процессы разработки и оценки их влияния на скорость и качество работы команд. **Будет большим плюсом:** * опыт работы с рекомендательными системами, поиском, ранжированием, персонализацией или рекламными технологиями * практический опыт работы в роли Data Scientist: формулирование и проверка ML-гипотез, оценка качества моделей и участие в A/B-тестах. * опыт эксплуатации real-time ML-систем на масштабе миллионов пользователей и при строгих требованиях к latency и availability. **Стек технологий:** Python, PySpark, PyTorch, RePlay, GigaChat, Airflow, MLflow, Kubernetes, Redis, Kafka, S3, FastAPI и другие инструменты для разработки, обучения, внедрения и мониторинга ML-моделей. Условия * комфортный современный офис рядом с м. Кутузовская * ежегодный пересмотр зарплаты и годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития   * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера

Требуется Data Engineer, который возьмёт на себя задачи, не связанные напрямую с аналитикой, включая автоматизацию создания отчётов, преобразование моделей в сервисы и оптимизацию запросов. Также необходимо наладить процесс код-ревью и внедрить CI/CD пайплайн для повышения эффективности разработки и поддержки проектов.

Location: TUR - Istanbul About Monster Energy: Forget about blending in. That's not our style. We're the risk takers, the trailblazers, the gamechangers. We're not perfect and we don't pretend to be. We're raw, unfiltered, and a bit unconventional. Our drive is just like our athletes, unrivaled. The power is in your hands to define what success looks like and where you want to take your career. It's not just about what we do, but about who we become on along the way. We are much more than a brand here. We are a way of life, a mindset. Join us. A Day in the Life: As Engineering Manager, you'll lead the execution of packaging and manufacturing initiatives across the APAC region, driving projects from concept through commercialization. You'll partner with bottlers, co-packers, suppliers, and cross-functional teams to deliver new product launches, innovation, capacity expansions, and process improvements. This role is responsible for ensuring projects are executed on time, within budget, and in compliance with Monster Energy's quality and packaging standards. You'll provide technical leadership, support packaging development and line trials, standardize specifications and processes, and identify opportunities to enhance efficiency, sustainability, and operational performance across the supply network. The Impact You'll Make: Develop and maintain relationships with all bottlers, co-packers and packaging suppliers to ensure consistency among standards and processes. Provide technical support on projects with regards to packaging development for new launches, innovation, new site expansion, specification standardization, sustainability, etc. Attend line trials and plant start-ups at co-packers and suppliers to ensure integrity of MEC product. Develop and maintain audit sheet, standard operating procedures (SOPs), work instructions, equipment specification, packaging standards. Assist in responding to customer complaints or inquires, develop action plans, Input of palletization details, weights and measures information within SAP system. Travel to all bottlers, co-packers and suppliers to evaluate processes and to ensure integrity of MEC product. Manage and update the current and archived container and packaging files. Who You Are: Prefer a Bachelor's Degree in the field of Engineering or related field of study Additional Experience Desired: More than 7 years of experience in manufacturing and packaging Additional Experience Desired: More than 7 years of experience in beverage industry Computer Skills Desired: Proficiency in Microsoft PowerPoint, Excel, Word, Project, and Outlook Additional Knowledge or Skills to be Successful in this role: Fluent English communication skills required, with the ability to effectively collaborate with stakeholders across the APAC region, including bottlers, co-packers, suppliers, and cross-functional teams. Read our privacy policy here: Privacy Policy

Location: Singapore DRW is a diversified trading firm with over 3 decades of experience bringing sophisticated technology and exceptional people together to operate in markets around the world. We value autonomy and the ability to quickly pivot to capture opportunities, so we operate using our own capital and trading at our own risk. Headquartered in Chicago with offices throughout the U.S., Canada, Europe, and Asia, we trade a variety of asset classes including Fixed Income, ETFs, Equities, FX, Commodities and Energy across all major global markets. We have also leveraged our expertise and technology to expand into three non-traditional strategies: real estate, venture capital and cryptoassets. We operate with respect, curiosity and open minds. The people who thrive here share our belief that it’s not just what we do that matters–it's how we do it. DRW is a place of high expectations, integrity, innovation and a willingness to challenge consensus. As a Trade Support Engineer , you’ll have the opportunity to learn and grow while supporting our trading technology. You’ll work closely with trading, infrastructure, and software development teams to keep systems running smoothly. You’ll gain experience working with exchanges, traders, and developers to help resolve technical issues and assist with ongoing improvements. Qualifications & Skills: 1–3 years of experience in trade support, systems administration, site reliability engineering, or a related technical role (internships or projects count!) Bachelor’s Degree in Computer Science, Engineering, or related STEM field Basic understanding of networking concepts (TCP/IP, DNS, DHCP) and ability to use common troubleshooting tools Exposure to containerized applications (e.g., Docker, Kubernetes) is a plus Ability to think through and troubleshoot technical problems under time pressure Clear communication skills, written and verbal Familiarity with scripting languages (e.g., Python, Bash, PowerShell) for basic automation tasks Experience with Linux/Unix environments (basic command line skills) What You’ll Be Working On: Keeping trading systems healthy and available by proactively spotting and resolving potential issues Responding to trader and engineer request with both urgency and understanding of the bigger operational context Participating in the onboarding of new desks and systems, thinking ahead about how they’ll fit into the broader ecosystem Learning the trading environment deeply so you can anticipate challenges and suggest improvements What we Value: Intent over Instruction: we’ll share what we’re trying to achieve; you’ll help determine the best way to get there Responsibility and trustworthiness: you follow through and communicate progress and results clearly Continuous learning and adaptability: when conditions change, you adjust while keeping the mission in mind Collaboration: you seek input when needed but are comfortable making informed decisions yourself About You: You enjoy diagnosing problems and thinking creatively about solutions You’re excited about fast-moving environments and want to see your work make an impact quickly You learn by doing and feel comfortable diving into unfamiliar technology For more information about DRW's processing activities and our use of job applicants' data, please view our Privacy Notice at https://drw.com/privacy-notice . California residents, please review the California Privacy Notice for information about certain legal rights at https://drw.com/california-privacy-notice . #LI-PR1

Location: Malaysia Job Overview We are seeking a highly experienced Senior Linux Infrastructure Engineer with deep expertise in Linux administration, bare metal infrastructure, enterprise storage, and next-generation AI Factory / GPU infrastructure platforms . This role is focused on designing, deploying, operating, and troubleshooting large-scale Linux-based infrastructure that powers both traditional enterprise workloads and modern AI/ML environments. This is not a DevOps-focused role . We already have a dedicated DevOps team and are looking for an engineer with extensive hands-on experience in Bare Metal as a Service (BMaaS), GPU infrastructure, high-performance storage, data center operations, and enterprise Linux platforms . The ideal candidate will have experience building and managing infrastructure from the hardware layer up, including servers, networking, storage, GPU clusters, and AI-ready platforms. They should be comfortable working with high-performance computing (HPC), AI Factory environments, and large-scale Linux deployments where performance, reliability, and operational excellence are critical. Key Responsibilities & Required Skills Linux & Bare Metal Infrastructure Expert-level Linux administration (Ubuntu required; Red Hat and SUSE preferred) Deep expertise in bare metal server deployment, architecture, provisioning, and lifecycle management Experience operating Bare Metal as a Service (BMaaS) platforms and large-scale infrastructure environments Strong understanding of server hardware, including: BIOS/UEFI RAID controllers Firmware management iLO/iDRAC/IPMI NICs and SmartNICs HBA cards Hardware diagnostics and troubleshooting Experience designing, implementing, and supporting enterprise Linux infrastructure at scale AI Factory & GPU Infrastructure Experience deploying and managing GPU-accelerated infrastructure for AI/ML workloads Understanding of NVIDIA GPU technologies including: A100, H100, H200, B200, or equivalent GPU platforms NVIDIA DGX and OEM GPU servers GPU provisioning and lifecycle management GPU monitoring and performance optimization Knowledge of AI Factory architecture and infrastructure requirements Experience supporting GPU clusters, AI training environments, and high-performance computing (HPC) workloads Understanding of: GPU resource allocation and scheduling Multi-GPU systems GPU networking requirements High-bandwidth, low-latency infrastructure design Familiarity with NVIDIA ecosystem technologies such as: CUDA NCCL GPUDirect Storage NVIDIA Fabric Manager NVIDIA Base Command (preferred) Enterprise Storage & Data Platforms Advanced Linux storage administration: LVM XFS, EXT4 NFS iSCSI Fibre Channel SAN Multipath I/O Strong hands-on experience with Ceph , including: Cluster architecture MON, OSD, MDS RBD, CephFS, RGW Capacity planning Performance tuning Failure recovery Experience with high-performance AI storage platforms such as: WEKA VAST Data Dell PowerScale Pure Storage FlashBlade NetApp Understanding of: NVMe-over-Fabrics (NVMe-oF) RDMA GPUDirect Storage Parallel file systems AI data pipelines Networking & Infrastructure Strong networking knowledge: Bonding VLANs Routing MTU optimization DNS DHCP Experience with high-performance data center networking: 100G/200G/400G Ethernet RoCE RDMA Spine-Leaf architectures Familiarity with NVIDIA Spectrum-X, Mellanox/NVIDIA ConnectX adapters, or equivalent technologies Strong understanding of Layer 2 and Layer 3 infrastructure design and troubleshooting Operations & Reliability Experience with high availability, clustering, and disaster recovery Strong troubleshooting skills across: Linux operating systems Hardware platforms GPU infrastructure Networking Enterprise storage Experience supporting mission-critical production environments Bash and Python scripting for automation and operational efficiency Experience creating operational documentation, runbooks, and infrastructure standards Understanding of AI infrastructure design and reference architectures AI cloud integration for workloads SOP and runbook development and maintenance Incident, problem, and capacity management Business continuity and disaster recovery planning for AI workloads Proactive risk identification and mitigation to avoid business impact Nice to Have Kubernetes infrastructure (especially AI/ML and GPU integration) KVM, VMware, OpenShift Virtualization, or similar virtualization platforms Ansible automation NVIDIA Base Command Manager Slurm or HPC workload schedulers Observability and monitoring platforms (Prometheus, Grafana, OpenTelemetry) Data Center Infrastructure Management (DCIM) tools IPAM solutions AWS, Azure, or hybrid cloud exposure We Are Not Looking For Candidates whose experience is primarily CI/CD pipeline engineering Engineers focused mainly on Terraform, GitOps, or application delivery pipelines Cloud-only administrators with limited bare metal, storage, or hardware experience Professionals whose primary expertise is software development rather than infrastructure engineering Ideal Candidate Someone who has spent years designing, building, and operating enterprise Linux environments, large-scale bare metal infrastructure, storage platforms, and modern AI Factory environments. The ideal candidate understands how to deploy and manage GPU-enabled infrastructure, BMaaS platforms, enterprise storage, and high-performance networking while solving complex operating system, hardware, storage, and AI infrastructure challenges. DevOps experience is a plus, but deep Linux, infrastructure, storage, BMaaS, and AI Factory expertise is the primary requirement.

Location: Singapore Job Overview We are seeking a highly experienced Senior Linux Infrastructure Engineer with deep expertise in Linux administration, bare metal infrastructure, enterprise storage, and next-generation AI Factory / GPU infrastructure platforms . This role is focused on designing, deploying, operating, and troubleshooting large-scale Linux-based infrastructure that powers both traditional enterprise workloads and modern AI/ML environments. This is not a DevOps-focused role . We already have a dedicated DevOps team and are looking for an engineer with extensive hands-on experience in Bare Metal as a Service (BMaaS), GPU infrastructure, high-performance storage, data center operations, and enterprise Linux platforms . The ideal candidate will have experience building and managing infrastructure from the hardware layer up, including servers, networking, storage, GPU clusters, and AI-ready platforms. They should be comfortable working with high-performance computing (HPC), AI Factory environments, and large-scale Linux deployments where performance, reliability, and operational excellence are critical. Key Responsibilities & Required Skills Linux & Bare Metal Infrastructure Expert-level Linux administration (Ubuntu required; Red Hat and SUSE preferred) Deep expertise in bare metal server deployment, architecture, provisioning, and lifecycle management Experience operating Bare Metal as a Service (BMaaS) platforms and large-scale infrastructure environments Strong understanding of server hardware, including: BIOS/UEFI RAID controllers Firmware management iLO/iDRAC/IPMI NICs and SmartNICs HBA cards Hardware diagnostics and troubleshooting Experience designing, implementing, and supporting enterprise Linux infrastructure at scale AI Factory & GPU Infrastructure Experience deploying and managing GPU-accelerated infrastructure for AI/ML workloads Understanding of NVIDIA GPU technologies including: A100, H100, H200, B200, or equivalent GPU platforms NVIDIA DGX and OEM GPU servers GPU provisioning and lifecycle management GPU monitoring and performance optimization Knowledge of AI Factory architecture and infrastructure requirements Experience supporting GPU clusters, AI training environments, and high-performance computing (HPC) workloads Understanding of: GPU resource allocation and scheduling Multi-GPU systems GPU networking requirements High-bandwidth, low-latency infrastructure design Familiarity with NVIDIA ecosystem technologies such as: CUDA NCCL GPUDirect Storage NVIDIA Fabric Manager NVIDIA Base Command (preferred) Enterprise Storage & Data Platforms Advanced Linux storage administration: LVM XFS, EXT4 NFS iSCSI Fibre Channel SAN Multipath I/O Strong hands-on experience with Ceph , including: Cluster architecture MON, OSD, MDS RBD, CephFS, RGW Capacity planning Performance tuning Failure recovery Experience with high-performance AI storage platforms such as: WEKA VAST Data Dell PowerScale Pure Storage FlashBlade NetApp Understanding of: NVMe-over-Fabrics (NVMe-oF) RDMA GPUDirect Storage Parallel file systems AI data pipelines Networking & Infrastructure Strong networking knowledge: Bonding VLANs Routing MTU optimization DNS DHCP Experience with high-performance data center networking: 100G/200G/400G Ethernet RoCE RDMA Spine-Leaf architectures Familiarity with NVIDIA Spectrum-X, Mellanox/NVIDIA ConnectX adapters, or equivalent technologies Strong understanding of Layer 2 and Layer 3 infrastructure design and troubleshooting Operations & Reliability Experience with high availability, clustering, and disaster recovery Strong troubleshooting skills across: Linux operating systems Hardware platforms GPU infrastructure Networking Enterprise storage Experience supporting mission-critical production environments Bash and Python scripting for automation and operational efficiency Experience creating operational documentation, runbooks, and infrastructure standards Understanding of AI infrastructure design and reference architectures AI cloud integration for workloads SOP and runbook development and maintenance Incident, problem, and capacity management Business continuity and disaster recovery planning for AI workloads Proactive risk identification and mitigation to avoid business impact Nice to Have Kubernetes infrastructure (especially AI/ML and GPU integration) KVM, VMware, OpenShift Virtualization, or similar virtualization platforms Ansible automation NVIDIA Base Command Manager Slurm or HPC workload schedulers Observability and monitoring platforms (Prometheus, Grafana, OpenTelemetry) Data Center Infrastructure Management (DCIM) tools IPAM solutions AWS, Azure, or hybrid cloud exposure We Are Not Looking For Candidates whose experience is primarily CI/CD pipeline engineering Engineers focused mainly on Terraform, GitOps, or application delivery pipelines Cloud-only administrators with limited bare metal, storage, or hardware experience Professionals whose primary expertise is software development rather than infrastructure engineering Ideal Candidate Someone who has spent years designing, building, and operating enterprise Linux environments, large-scale bare metal infrastructure, storage platforms, and modern AI Factory environments. The ideal candidate understands how to deploy and manage GPU-enabled infrastructure, BMaaS platforms, enterprise storage, and high-performance networking while solving complex operating system, hardware, storage, and AI infrastructure challenges. DevOps experience is a plus, but deep Linux, infrastructure, storage, BMaaS, and AI Factory expertise is the primary requirement.

Location: Serbia Job Overview We are seeking a highly experienced Senior Linux Infrastructure Engineer with deep expertise in Linux administration, bare metal infrastructure, enterprise storage, and next-generation AI Factory / GPU infrastructure platforms . This role is focused on designing, deploying, operating, and troubleshooting large-scale Linux-based infrastructure that powers both traditional enterprise workloads and modern AI/ML environments. This is not a DevOps-focused role . We already have a dedicated DevOps team and are looking for an engineer with extensive hands-on experience in Bare Metal as a Service (BMaaS), GPU infrastructure, high-performance storage, data center operations, and enterprise Linux platforms . The ideal candidate will have experience building and managing infrastructure from the hardware layer up, including servers, networking, storage, GPU clusters, and AI-ready platforms. They should be comfortable working with high-performance computing (HPC), AI Factory environments, and large-scale Linux deployments where performance, reliability, and operational excellence are critical. Key Responsibilities & Required Skills Linux & Bare Metal Infrastructure Expert-level Linux administration (Ubuntu required; Red Hat and SUSE preferred) Deep expertise in bare metal server deployment, architecture, provisioning, and lifecycle management Experience operating Bare Metal as a Service (BMaaS) platforms and large-scale infrastructure environments Strong understanding of server hardware, including: BIOS/UEFI RAID controllers Firmware management iLO/iDRAC/IPMI NICs and SmartNICs HBA cards Hardware diagnostics and troubleshooting Experience designing, implementing, and supporting enterprise Linux infrastructure at scale AI Factory & GPU Infrastructure Experience deploying and managing GPU-accelerated infrastructure for AI/ML workloads Understanding of NVIDIA GPU technologies including: A100, H100, H200, B200, or equivalent GPU platforms NVIDIA DGX and OEM GPU servers GPU provisioning and lifecycle management GPU monitoring and performance optimization Knowledge of AI Factory architecture and infrastructure requirements Experience supporting GPU clusters, AI training environments, and high-performance computing (HPC) workloads Understanding of: GPU resource allocation and scheduling Multi-GPU systems GPU networking requirements High-bandwidth, low-latency infrastructure design Familiarity with NVIDIA ecosystem technologies such as: CUDA NCCL GPUDirect Storage NVIDIA Fabric Manager NVIDIA Base Command (preferred) Enterprise Storage & Data Platforms Advanced Linux storage administration: LVM XFS, EXT4 NFS iSCSI Fibre Channel SAN Multipath I/O Strong hands-on experience with Ceph , including: Cluster architecture MON, OSD, MDS RBD, CephFS, RGW Capacity planning Performance tuning Failure recovery Experience with high-performance AI storage platforms such as: WEKA VAST Data Dell PowerScale Pure Storage FlashBlade NetApp Understanding of: NVMe-over-Fabrics (NVMe-oF) RDMA GPUDirect Storage Parallel file systems AI data pipelines Networking & Infrastructure Strong networking knowledge: Bonding VLANs Routing MTU optimization DNS DHCP Experience with high-performance data center networking: 100G/200G/400G Ethernet RoCE RDMA Spine-Leaf architectures Familiarity with NVIDIA Spectrum-X, Mellanox/NVIDIA ConnectX adapters, or equivalent technologies Strong understanding of Layer 2 and Layer 3 infrastructure design and troubleshooting Operations & Reliability Experience with high availability, clustering, and disaster recovery Strong troubleshooting skills across: Linux operating systems Hardware platforms GPU infrastructure Networking Enterprise storage Experience supporting mission-critical production environments Bash and Python scripting for automation and operational efficiency Experience creating operational documentation, runbooks, and infrastructure standards Understanding of AI infrastructure design and reference architectures AI cloud integration for workloads SOP and runbook development and maintenance Incident, problem, and capacity management Business continuity and disaster recovery planning for AI workloads Proactive risk identification and mitigation to avoid business impact Nice to Have Kubernetes infrastructure (especially AI/ML and GPU integration) KVM, VMware, OpenShift Virtualization, or similar virtualization platforms Ansible automation NVIDIA Base Command Manager Slurm or HPC workload schedulers Observability and monitoring platforms (Prometheus, Grafana, OpenTelemetry) Data Center Infrastructure Management (DCIM) tools IPAM solutions AWS, Azure, or hybrid cloud exposure We Are Not Looking For Candidates whose experience is primarily CI/CD pipeline engineering Engineers focused mainly on Terraform, GitOps, or application delivery pipelines Cloud-only administrators with limited bare metal, storage, or hardware experience Professionals whose primary expertise is software development rather than infrastructure engineering Ideal Candidate Someone who has spent years designing, building, and operating enterprise Linux environments, large-scale bare metal infrastructure, storage platforms, and modern AI Factory environments. The ideal candidate understands how to deploy and manage GPU-enabled infrastructure, BMaaS platforms, enterprise storage, and high-performance networking while solving complex operating system, hardware, storage, and AI infrastructure challenges. DevOps experience is a plus, but deep Linux, infrastructure, storage, BMaaS, and AI Factory expertise is the primary requirement.

Location: Poland Job Overview We are seeking a highly experienced Senior Linux Infrastructure Engineer with deep expertise in Linux administration, bare metal infrastructure, enterprise storage, and next-generation AI Factory / GPU infrastructure platforms . This role is focused on designing, deploying, operating, and troubleshooting large-scale Linux-based infrastructure that powers both traditional enterprise workloads and modern AI/ML environments. This is not a DevOps-focused role . We already have a dedicated DevOps team and are looking for an engineer with extensive hands-on experience in Bare Metal as a Service (BMaaS), GPU infrastructure, high-performance storage, data center operations, and enterprise Linux platforms . The ideal candidate will have experience building and managing infrastructure from the hardware layer up, including servers, networking, storage, GPU clusters, and AI-ready platforms. They should be comfortable working with high-performance computing (HPC), AI Factory environments, and large-scale Linux deployments where performance, reliability, and operational excellence are critical. Key Responsibilities & Required Skills Linux & Bare Metal Infrastructure Expert-level Linux administration (Ubuntu required; Red Hat and SUSE preferred) Deep expertise in bare metal server deployment, architecture, provisioning, and lifecycle management Experience operating Bare Metal as a Service (BMaaS) platforms and large-scale infrastructure environments Strong understanding of server hardware, including: BIOS/UEFI RAID controllers Firmware management iLO/iDRAC/IPMI NICs and SmartNICs HBA cards Hardware diagnostics and troubleshooting Experience designing, implementing, and supporting enterprise Linux infrastructure at scale AI Factory & GPU Infrastructure Experience deploying and managing GPU-accelerated infrastructure for AI/ML workloads Understanding of NVIDIA GPU technologies including: A100, H100, H200, B200, or equivalent GPU platforms NVIDIA DGX and OEM GPU servers GPU provisioning and lifecycle management GPU monitoring and performance optimization Knowledge of AI Factory architecture and infrastructure requirements Experience supporting GPU clusters, AI training environments, and high-performance computing (HPC) workloads Understanding of: GPU resource allocation and scheduling Multi-GPU systems GPU networking requirements High-bandwidth, low-latency infrastructure design Familiarity with NVIDIA ecosystem technologies such as: CUDA NCCL GPUDirect Storage NVIDIA Fabric Manager NVIDIA Base Command (preferred) Enterprise Storage & Data Platforms Advanced Linux storage administration: LVM XFS, EXT4 NFS iSCSI Fibre Channel SAN Multipath I/O Strong hands-on experience with Ceph , including: Cluster architecture MON, OSD, MDS RBD, CephFS, RGW Capacity planning Performance tuning Failure recovery Experience with high-performance AI storage platforms such as: WEKA VAST Data Dell PowerScale Pure Storage FlashBlade NetApp Understanding of: NVMe-over-Fabrics (NVMe-oF) RDMA GPUDirect Storage Parallel file systems AI data pipelines Networking & Infrastructure Strong networking knowledge: Bonding VLANs Routing MTU optimization DNS DHCP Experience with high-performance data center networking: 100G/200G/400G Ethernet RoCE RDMA Spine-Leaf architectures Familiarity with NVIDIA Spectrum-X, Mellanox/NVIDIA ConnectX adapters, or equivalent technologies Strong understanding of Layer 2 and Layer 3 infrastructure design and troubleshooting Operations & Reliability Experience with high availability, clustering, and disaster recovery Strong troubleshooting skills across: Linux operating systems Hardware platforms GPU infrastructure Networking Enterprise storage Experience supporting mission-critical production environments Bash and Python scripting for automation and operational efficiency Experience creating operational documentation, runbooks, and infrastructure standards Understanding of AI infrastructure design and reference architectures AI cloud integration for workloads SOP and runbook development and maintenance Incident, problem, and capacity management Business continuity and disaster recovery planning for AI workloads Proactive risk identification and mitigation to avoid business impact Nice to Have Kubernetes infrastructure (especially AI/ML and GPU integration) KVM, VMware, OpenShift Virtualization, or similar virtualization platforms Ansible automation NVIDIA Base Command Manager Slurm or HPC workload schedulers Observability and monitoring platforms (Prometheus, Grafana, OpenTelemetry) Data Center Infrastructure Management (DCIM) tools IPAM solutions AWS, Azure, or hybrid cloud exposure We Are Not Looking For Candidates whose experience is primarily CI/CD pipeline engineering Engineers focused mainly on Terraform, GitOps, or application delivery pipelines Cloud-only administrators with limited bare metal, storage, or hardware experience Professionals whose primary expertise is software development rather than infrastructure engineering Ideal Candidate Someone who has spent years designing, building, and operating enterprise Linux environments, large-scale bare metal infrastructure, storage platforms, and modern AI Factory environments. The ideal candidate understands how to deploy and manage GPU-enabled infrastructure, BMaaS platforms, enterprise storage, and high-performance networking while solving complex operating system, hardware, storage, and AI infrastructure challenges. DevOps experience is a plus, but deep Linux, infrastructure, storage, BMaaS, and AI Factory expertise is the primary requirement.

Location: San Francisco, CA; Sunnyvale, CA Workplace: Hybrid At Niantic Spatial, we’re building the future of physical AI. Powered by a proprietary database of over 30 billion posed images, our groundbreaking mapping technology unlocks a new dimension of interaction and spatial intelligence that helps both humans and machines better understand, represent, navigate, and engage with the real environment. Join us as we build the real-world foundational models for physical AI, so people and machines can work efficiently and safely together. As a Senior Computer Vision Engineer on the Applied Algorithms team, you will own significant parts of our Localization technology. As a member of this team, you will be responsible for building the Map representing a living geospatial world model, as well as the centimeter-level accuracy Visual Positioning System (VPS) that allows people and robots to explore the real world. Part of the role will be bridging the gap between R&D and production, turning complex geospatial data into a persistent sense of space and enabling the next generation of spatial AI. RESPONSIBILITIES - System Architecture: Design, develop, and maintain production-grade computer vision systems that power Niantic Spatial’s VPS (Visual Positioning System) and 3D mapping pipelines. - Algorithmic Innovation: Refine and scale algorithms for our VPS system including, SfM (Structure from Motion) and Feed Forwards models, efficient feature and descriptor extraction to have the best possible accuracy and recall. Moving algorithms from research concepts to hardened production code that can scale for our map of the world. - Performance Engineering: Optimize complex ML and CV code for maximum efficiency in cloud and mobile environments, ensuring low latency and high-performance execution on GPU/CPU. - Benchmarking & Evaluation: Create and own the tools and frameworks used to evaluate the quality of our spatial grounding and 3D maps against ground-truth data. - Technical Leadership: Lead technical design reviews, mentor junior engineers, and serve as a team anchor for resolving complex technical disagreements within the mapping stack. - Cross-Functional Delivery: Collaborate with Product, Research, and Operations teams to ensure that state-of-the-art computer vision solutions translate into delightful user experiences. REQUIREMENTS - Education: BS, MS, or PhD in Computer Science, Robotics, Computer Vision, or a related technical field (or equivalent professional experience). - Years of Experience: 5+ years of experience developing and shipping algorithms for image processing, computer vision, or 3D reconstruction. - Coding Proficiency: Expert-level proficiency in python and/or C++. - Domain Expertise: Proven track record in designing solutions for Structure from Motion (SfM), VPS or 3D mapping.. - Frameworks & Tools: Deep experience with Deep Learning frameworks (PyTorch or JAX) and version control (Git). - Required in-office days: 3 days per week Plus If: - Experience in planning and leading technical projects from inception to production. - Significant contributions to open-source CV libraries (OpenCV, COLMAP, etc.). - Experience with CUDA or shader programming for performance optimization. Candidate Privacy Policy I understand that by submitting my job application, the information I provide as part of that application will be used in accordance with Niantic Spatial’s Privacy Notice for Job Applicants and Candidates https://www.nianticspatial.com/applicant-privacy-notice. If required by law, by submitting my job application I consent to the processing of my information as described in that Notice, including processing information I voluntarily disclose to Niantic Spatial, such as health or medical information, race or ethnicity data, and sexual orientation data and, in limited circumstances sharing information with third parties such as references and other third parties that assist in the hiring process. Niantic Spatial is an equal opportunity employer. Individuals seeking employment at Niantic Spatial are considered without regard to race, color, ancestry, national origin, religion, creed, age, gender (including pregnancy, childbirth, breastfeeding or related medical conditions), marital status, physical or mental disability, medical condition, genetic information, military or veteran status, gender identity, gender expression, sexual orientation, or any other protected category under applicable laws. Niantic Spatial, will also consider qualified applicants with criminal histories in accordance with applicable laws. Please contact your recruiter if you want to request an accommodation for the job application or interview process.

Location: New York, New York, United States About Turing Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI labs to generate high-quality datasets, reinforcement learning environments, and frontier research benchmarks that improve model capabilities in software engineering, enterprise knowledge work, and advanced STEM reasoning. In software engineering, Turing is the largest and longest-running data provider in the category. Turing also works with Fortune 500 enterprises across financial services, life sciences, healthcare, retail, automotive, and CPG to build and deploy end-to-end agentic AI systems inside mission-critical workflows. By operating on both sides, Turing closes the loop between frontier research and enterprise deployment, turning real-world deployment signals into better data, evaluations, and more capable models. Learn more at www.turing.com . Role: Forward-Deployed AI Engineer — Enterprise AI Location: New York City Experience Level: 8+ years Compensation: $200,000–$250,000 base salary + equity Work Model: In-office, typically three to five days per week, depending on client requirements Why Join Enterprise AI? Enterprise AI is a distinct business unit within Turing, operating with the speed, focus, and ownership of a rapidly scaling startup. When it comes to applied AI in the workplace, you’ll define the frontier by building beyond it. You’ll join the initial team of Forward-Deployed Engineers, shaping how we work while solving enterprise-grade problems for the world’s leading enterprises. As a Forward-Deployed AI Engineer, you’ll own outcomes end to end—from identifying high-value customer problems and shaping architecture to building production systems and working directly with senior client leaders. You’ll develop generative AI solutions in the real world, driving measurable impact and return on investment within large institutions. Enterprise AI is intense, intellectually challenging work. It won’t be for everyone, but it may be exactly the challenge you’re looking for. As a Forward-Deployed Engineer, You Will Find the Problem Worth Solving Work directly with customers to navigate ambiguity, uncover high-value opportunities, challenge assumptions, and translate complex business needs into clear technical direction. This requires curiosity and business acumen as you discover business workflows and jointly define the outcomes an AI solution should drive. Build Beyond the Frontier Architect and build production AI systems, from multi-agent workflows, orchestration, tool use, context engineering, memory and state, and retrieval-augmented generation (RAG) to the APIs, data pipelines, integrations, and distributed systems that make them reliable at scale. Own the Outcome You won’t simply be given a list of responsibilities—you’ll have the authority to own the outcome for your client and make a real-world impact through your work. You’ll lead solutions from discovery through deployment and measurable impact while staying hands-on and owning evaluation, observability, security, reliability, latency, scalability, and cost. The Anti-Sell High ownership and intensity: This role requires exceptional ownership and accountability. You’ll work with clients who have uncompromising expectations for quality, making this a demanding environment—and an ideal proving ground for people who seek difficult, high-impact challenges. Client-first and in-office: Our work is client-first, which means in-person collaboration may be required either at the client site or at our planned Manhattan office near Madison Square Park. Depending on your assignment and client needs, you should expect to work in person three to five days per week and be based in the New York City area or within a reasonable commuting distance. You’ll help write the playbook: Enterprise AI operates like a startup. You may not always have an established process or playbook because you’ll be expected to apply your expertise, judgment, and technical depth to create one. Our Forward-Deployed Engineers Typically Have Deep production engineering experience: Typically 8+ years building and operating sophisticated software systems, with expertise in Python, backend systems, APIs, databases, distributed systems, system design, and cloud infrastructure. Hands-on agentic AI experience: Experience building production LLM and agent systems using orchestration, tool use, context engineering, memory and state, retrieval, and multistep workflows. End-to-end systems ownership: A track record of taking complex systems from architecture through deployment and scale, with a practical understanding of evaluation, observability, reliability, security, guardrails, latency, scalability, and cost. Exceptional customer and technical leadership: The ability to enter unfamiliar environments, earn trust quickly, navigate ambiguity, challenge assumptions constructively, and communicate effectively with senior engineers and business executives. Client-ready communication: The ability to explain technical concepts clearly, navigate ambiguity, work with stakeholders, and remain calm under pressure. Don’t Meet Every Qualification Yet? If you don’t meet every qualification for this role, we encourage you to apply to our Forward-Deployed AI Engineer (In Training) opportunity. This role is designed for high-potential software engineers and full-stack developers who are building the production engineering, applied AI, and client-delivery skills required to become Forward-Deployed AI Engineers at Turing. Values We are client first: We put our clients at the center of everything we do, because their success is the ultimate measure of our value. We work at Start-Up Speed: We move fast, stay agile and favor action because momentum is the foundation of perfection We are AI forward: We help our clients build the future of Al and implement it in our own roles and workflow to amplify productivity. Advantages of joining Turing Work at the frontier of AI , helping the world’s leading AI labs improve their most advanced models by building expert datasets, RL environments, and first-of-a-kind benchmarks. Contribute to leading-edge AI research and showcase your work at top conferences such as ICLR, ICML, and NeurIPS. Bring frontier AI innovation to the enterprise , applying lessons learned from leading AI labs to solve real-world business challenges. Collaborate with and learn from exceptional colleagues with deep AI experience from Google, Meta, Amazon, and other leading technology companies. Move at the pace of AI innovation , with the speed, ownership, and impact of a startup. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace  and celebrate authenticity, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here .