Вакансии

Location: Сочи Центр Инноваций создает передовые решения на стыке технологий и бизнеса за счет фундаментальных знаний, примененных к рынку в виде прикладного исследования, которое рождает технологию, для дальнейшей интеграции в продукт. Мы реализуем полный цикл разработки: от генерации идей, проведения исследований, до создания прототипов, MVP и запуска продуктов и сервисов. Взаимодействуем с клиентами, партнерами, продуктовыми командами, маркетингом, университетами. Обязанности ### Обязанности * сопровождение и развитие автоматизированной системы (АС) AI Hub * коммуникация с пользователями АС * решение инцидентов и проблем * решение задач по развитию АС * поддержка смежных систем и команд (АС, агенты и т.п.) * участие в цикле разработки: от генерации идей, проведения исследований, до создания прототипов, MVP и запуска продуктов и сервисов. * взаимодействие со всеми членами команды и другими подразделениями, а также партнерами для достижения целей проектных инициатив. Требования ### Требования * опыт от 2 лет на позиции DevOps/SRE  * степень бакалавра или магистра по профильной специальности * опыт работы в поддержке и эксплуатации автоматизированных систем * знание методологий SDLC, Agile и соответствующих инструментов * понимание технологий контейнеризации и виртуализации, Cloud Native, микросервисной архитектуры, сетевых технологий * понимание объектно-ориентированного программирования и design patterns * уверенное владение инструментами контроля версий, такими, как Git * знания Linux и сетевых протоколов * опыт работы с CI/CD (GitLab/Jenkins/GitHub Actions) * навыки мониторинга и логирования (Prometheus, Grafana, VictoriaMetrics) и написания скриптов на Python/Bash. ### Будет плюсом * опыт настройки и сопровождения инференса, MLOps * навыки работы с генеративными AI-моделями, опыт создания AI-агентов и использования их в работе * владение английским для свободного чтения материалов в своей профессиональной области. Условия ### Условия * гибридный формат работы из офиса в г. Сочи * годовой бонус и ежегодный пересмотр * ДМС с первого дня + стоматологию и льготное страхование для семьи * корпоративный университет Сбера, внутреннюю образовательную платформу, участие в IT-конференциях * льготную ипотеку в Сбере, корпоративную пенсионную программу, подписку СберПрайм с возможностью совместного использования на трёх близких, скидки от партнеров и сервисов группы компаний.

Полный цикл разработки и внедрения ML-моделей в продакшен, мониторинг и ретренинг. Лидерство в реализации бизнес-задач.

Manage and maintain IT systems in a cloud first environment. Provide friendly and unflappable technical support.

Escalation Management & Complex Ticket Handling. Lead high-priority or complex support issues, including those involving multiple teams or external partners.

Location: Tel Aviv-Yafo, Tel Aviv District, Israel About Aidoc Aidoc is the market leader in healthcare AI and has developed the world's largest clinical frontier model, supporting nearly 50 million patients each year. Aidoc is deployed in more than 2,000 medical centers worldwide. Since our founding in 2016, Aidoc has raised over $500 million and achieved a record number of FDA-cleared solutions. Medical diagnosis is hitting a breaking point, where the number of physicians hasn't kept up with the rapidly growing patient load. This leads to misdiagnosis and treatment delays that result in hundreds of thousands of deaths every year. Aidoc assists clinicians by precisely highlighting diseases directly on medical images, preventing misdiagnosis and ensuring that urgent patients receive the immediate attention they need. About this role We are looking for an experienced Engineering Team Lead to build and lead the FirstRead team , a new product-focused team within Aidoc’s Imaging Platform group. FirstRead, Aidoc’s report-generation initiative, is one of the company’s most important and high-potential new products. We have decided to found this team, following a highly successful product beta, in which our AI drafted thousands of full radiology reports, covering hundreds of different clinical findings with near-human accuracy. The product is evolving rapidly, and so will the team around it. With the opportunity to shape the product and its technical direction from the ground up, the FirstRead team will be accountable for the product outcome end-to-end . The team will work across the stack, using existing platform capabilities where possible, evolving them where needed, and building new services and capabilities when required. This is a role for a TL who enjoys shaping new products and turning evolving opportunities into clear technical direction. The team’s scope, architecture, priorities, and  longer-term mission will evolve as we learn. You will play a central role not only in delivering FirstRead, but also in building the team itself and shaping how product-focused teams operate within Imaging Platform . You will work closely with Product, AI teams, and other engineering teams across Imaging Platform to turn rapidly evolving product needs into working software, while balancing speed, technical quality, and long-term platform considerations. Responsibilities End-to-End Ownership: Lead the engineering effort required to make FirstRead successful, across engineering disciplines including  backend, frontend, AI infrastructure, DICOM pipelines, existing platform services, and entirely new components. Creating Clarity in a Fast-Moving Environment: Turn changing product needs and incomplete information into technical direction, priorities, and executable plans. Continuously adapt as the product evolves. Technical Leadership: Drive architecture and technical decisions across a broad technical surface. Know when to move quickly, when to invest in robust solutions, and when to involve domain experts. Cross-Team Engineering: Work closely with other TLs and engineers across Imaging Platform. Enable the team to contribute to existing services and platforms while respecting their architecture, ownership, and long-term direction. Team Building: Build the FirstRead engineering team, establish its culture and ways of working, develop engineers, and help shape its longer-term mission as a product-focused team. Delivery and Product Partnership: Partner closely with Product and other stakeholders, taking ownership of turning evolving product direction into concrete engineering execution. Engineering Excellence: Maintain strong engineering standards while operating in a fast-moving environment, balancing experimentation and delivery speed with reliability, maintainability, and scalability. Requirements Senior hands-on software engineering experience , including experience building complex systems end-to-end. Exceptional track record leading engineering teams , while remaining actively involved in technical and architectural decisions. Strong sense of urgency, with the ability to drive execution and maintain momentum in fast-paced, high-priority projects. Proven ability to lead projects in ambiguous, rapidly changing environments , where requirements and technical direction are not fully defined upfront. Broad technical experience and passion for working across different areas of the stack rather than specializing exclusively in a single domain. Strong system design and software engineering fundamentals, with experience designing and building distributed production systems. Demonstrated ability to take ownership of an outcome from early concept through implementation, production, and continued evolution . Excellent collaboration and communication skills, with demonstrated ability to work effectively across engineering teams and organizational boundaries. Strong product and business orientation, with the ability to balance technical considerations with speed, learning, and business impact. Strong leadership, mentorship, and team-building skills. Advantages Experience working in an environment with shared platforms and multiple engineering teams contributing to the same product. Experience across multiple areas such as backend, frontend, cloud infrastructure, data pipelines, or AI/ML systems. Experience with AI-powered products or production AI systems. Working at Aidoc Our Perks: Be part of something big - using cutting-edge technologies to transform the Healthcare industry (while saving patients’ lives) We work in a hybrid model, with our new offices located at 34 HaMasger Street in Tel Aviv and parking for employees. Amazing and healthy breakfasts and lunches prepared daily by our personal chef! Stocked up kitchen & meal card Wellness: Aidoc employees-only gym, plus Pilates, Yoga, and functional workouts classes. Amazing culture - collaborative, transparent & fun! Attractive compensation package & benefits Aidoc is deeply committed to creating an inclusive workplace, and to the principle of equal opportunity for all individuals. We prohibit discrimination and harassment based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other status protected by law.

Location: AU: Sydney (45 Clarence St) Workplace: Hybrid The role & impact As an Engineering Manager in our Machine Learning & AI team, you'll lead a high-performing, inclusive team of engineers building and operating AI-powered experiences and capabilities across Xero. The team's work spans traditional machine learning, from predictive models and intelligent automation through to LLM-powered and agentic AI experiences that bring new ways for customers to interact with and get value from Xero. - You'll lead engineering across a broad range of technology surfaces, from customer-facing AI experiences and intelligent product workflows to the backend services, platforms and infrastructure that power them. You'll help turn research, models and emerging AI capabilities into reliable, secure and scalable production products that make a real difference for millions of small businesses. - You'll balance predictable delivery with a relentless focus on engineering quality and system health, building our maturity across MLOps, LLMOps, observability, evaluation and the operational practices needed to run AI systems in production. As AI continues to evolve, you'll also champion AI-native engineering practices and help your team adapt how we design, build, test and operate software. - Working closely with senior individual contributors, including staff and principal engineers, you'll help shape technical direction while focusing on team growth, delivery excellence and sustainable engineering practices. This is a role for someone who wants to build both the systems and the people that enable Xero to deliver AI-powered products at scale. The team & how they connect You'll join Xero's Data & AI organisation, within the AI Products division, reporting to the GM of ML Engineering. The team partners closely with Applied Science, Data Engineering, Product and other engineering teams to bring AI capabilities from research and experimentation into production. Our scope goes beyond building and serving models. We build the end-to-end engineering capabilities that bring AI to life in Xero's products, spanning traditional ML-powered automation and predictions, conversational AI assistants, LLM-powered workflows and agentic experiences. This means working across the full stack, integrating AI into customer experiences and product workflows while building the front end & backend services, platforms and infrastructure needed to make those experiences safe, reliable and scalable. It's a collaborative, evolving space where you'll help translate advances in ML and generative AI into practical customer outcomes, while building the engineering foundations to support the next generation of AI-native products. The team is currently working on - Production ML and AI engineering: Building and operating the platforms, services and practices that support traditional ML models, generative AI and agentic systems in production. - JAX (Just Ask Xero): Powering Xero's conversational GenAI assistant and evolving the underlying capabilities to support increasingly sophisticated, context-aware and agentic experiences. - Intelligent financial workflows: Developing capabilities such as Automated Bank Reconciliation and smart document capture, using ML and AI to match transactions, extract information from bills and receipts, and automate complex tasks. - XeroForce: Building the platform for AI-powered workflows, enabling agents to plan and execute complex tasks across Xero’s products and supporting reliable, reusable agentic experiences at scale. - End-to-end AI product experiences: Integrating AI into customer-facing experiences and product workflows, alongside the backend services, orchestration and infrastructure that make them work. Where and how you can work We support a flexible working model that empowers you to balance your professional and personal life. You'll have the option to work in a hybrid capacity, combining the focus of remote work with the collaboration of our office spaces and team boost days to foster connection and alignment. Here are some of the things we're looking for, for this role - You'll bring a strong track record of leading, coaching and developing engineers, building team capability and creating an environment where people can do their best work. - You have strong technical judgement across architecture, system design and engineering trade-offs, and can guide decisions across a broad and evolving technology landscape. - You've translated business goals into executable engineering plans, balancing near-term delivery with long-term technical health and maintainability. - You're experienced in delivering roadmaps predictably in fast-paced, evolving environments, navigating ambiguity and adapting as priorities and technology change. - You understand what it takes to build and operate production-grade software, from customer-facing experiences and backend services through to distributed systems, platforms and infrastructure. - You stay hands-on when it counts, comfortable writing, reviewing or debugging production code to unblock your team, support an incident or help resolve a complex technical challenge. - Experience with machine learning, generative AI or AI-powered products is a bonus rather than a requirement. You don't need to be an ML specialist, but genuine curiosity about the space and an appetite to learn how these technologies are changing software engineering will help you thrive here. 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.

Location: Москва Мы - команда эксплуатации Управления централизованных технологических решений. Мы занимаемся сопровождением и повышением надежности автоматизированных систем, обеспечивающих хранение и интеллектуальную обработку документов по основным бизнес-процессам Банка с использованием ML-моделей и AI-решений. Обязанности * администрирование и сопровождение автоматизированных систем (АС) банка * устранение инцидентов и отклонений в работе АС * настройка и поддержка промышленного ландшафта и стенда ПСИ * внедрение новых версий прикладного программного обеспечения * организация мониторинга АС и его актуализация в ходе эксплуатации АС * реализация требований надежности и кибербезопасности, предъявляемых к АС. **Наш техстек:** Linux, K8S/OpenShift, Docker Kafka, PostgreSQL, Ignite , Redis Jira, Confluence, BitBucket, Jenkins, Nexus, Ansible Prometheus, Zabbix, Grafana, Dynatrace Требования * опыт сопровождения и администрирования автоматизированных систем от 1 года * навык работы в командной сроке Linux, знание базового набора команд * знание принципов организации и построения баз данных * знание SQL (простые/средней сложности запросы) * опыт работы с платформами контейнеризации K8S/Openshift. * понимание основ виртуализации инфраструктуры (VMWare, KVM) * навык автоматизации рутинных задач с использованием скриптовых языков * инструментальное владение AI для анализа, генерации и автоматизации * знание принципов ITIL/ITSM (опционально). Условия * офис - Москва, 2-й Южнопортовый проезд, 12а * гибридный формат работы - 4 дня в неделю офис, 1 день удаленный (по завершению исп. срока) * ежегодный пересмотр зарплаты, годовое премирование * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * расширенный ДМС, льготное страхование для семьи * ипотека для сотрудников (выгоднее до 7%) * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера.

Location: Москва GigaIDE Desktop — это интегрированная среда разработки, которая включает в себя: текстовый редактор, транслятор, средства автоматизации сборки, отладчик, средства совместной разработки для командной работы. Основное направление работ – сопровождение пользователей в рамках второй линии поддержки. Если ты опытный инженер, хочешь развиваться, любишь решать интересные задачи и улучшать жизнь пользователей - эта позиция для тебя. Дополнительные задачи – участие в релизном цикле. Обязанности ### Обязанности: * выполнение заявок на обслуживание и решение инцидентов от 1 линии * мониторинг инфраструктуры сервиса * разработка и поддержание в актуальном состоянии документации по типовым ошибкам и проблемам для 1 линии * оперативное реагирование и решение нештатных ситуаций, связанных с работой продукта * взаимодействие с командой разработки и смежными командами. Требования ### Мы ожидаем: * опыт работы в качестве инженера 2 линии поддержки от двух лет * понимание основ ITIL, ITSM, а также жизненного цикла производства и сопровождения ПО * опыт работы с системами мониторинга и алертинга (Zabbix/Grafana/Prometheus) * опыт работы с IntelliJ IDEA, Git, Jira, Confluence или аналогами * чтение и анализ логов работы приложений (ELK, Loki) * базовый опыт работы с Linux * опыт взаимодействия с REST API и реляционными базами данных (PostgreSQL) * опыт использования GigaChat, Kandinsky и аналогов в продуктах, навыки создания и использования AI-агентов * инструментальное владение AI для анализа, генерации и автоматизации. **Будет плюсом:** * опыт работы с технологиями контейнеризации и оркестрации (Docker, Kubernetes) * умение читать и писать код на Python/ Bash /Groovy. Условия ### **Мы предлагаем:** * гибридный формат работы * годовой бонус и ежегодный пересмотр зарплаты * статус аккредитованной ИТ-компании со всеми преимуществами * расширенный ДМС с первого дня и льготное страхование для семьи * корпоративный университет Сбера, внутренняя образовательная платформа, участие в IT-конференциях * офис с видом на набережную, зонами отдыха и спортзалом * 90 дней удаленной работы * льготная ипотека в Сбере, подписка Прайм с возможностью шеринга (совместного использования) на трёх близких, скидки от партнеров и сервисов группы компаний.

Задачи: — проектировать, валидировать и выводить на рынок готовые к производству батарейные системы. Требования: — опыт в области безопасности батарей, теплового менеджмента, литий-ионных батарей, анализа отказов, характеристики и валидации. Условия: — работа в офисе в Сан-Карлос, Калифорния. Технологии и инструменты: — battery management system (BMS)

Location: 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 Anthropic manages one of the largest and fastest-growing infrastructure fleets in the industry — spanning multiple accelerator families, cpu families and clouds. The Capacity Engineering team is responsible for making sure all our infrastructure resources are accounted for, well-utilized, and efficiently allocated. We own the data, tooling, and operational systems that let Anthropic plan, measure, and maximize utilization across first-party and third-party compute. As an engineer on Capacity Engineering, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on. You will be expected to write production-quality code every day, operate alongside Kubernetes-native infrastructure at meaningful scale, and directly influence decisions around one of Anthropic’s largest areas of spend. You’ll collaborate closely with research engineering, infrastructure, inference, and finance teams. The work requires someone who can move between data engineering, systems engineering, and observability with comfort — and who thrives in a high-autonomy, high-ambiguity environment. This is a pipeline role feeding four areas. Depending on your background and business priority, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap: Data platform Pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, normalize billing and usage across cloud providers, and serve the BigQuery tables the rest of the org queries against. Correctness, completeness, and latency are the job, not a footnote. Consumers range from research engineers to finance to leadership, so it's product work as much as engineering: defining schema contracts, making data discoverable, and figuring out what people actually need. Planning Knowing what the fleet has, where it's going, and what's in the way. Making the state of the fleet legible and actionable in real time: cluster health tooling, capacity planning platforms, alerting on occupancy drops and allocation problems, and systemic fixes to scheduling and fragmentation. Kubernetes operations on one side, cross-team coordination on the other. Efficiency Measuring and improving how effectively every major workload uses the hardware it runs on. Instrumenting utilization across training, inference, and eval systems, building benchmarking infrastructure, establishing per-config baselines, and working directly with system-owning teams to close the gaps. The metric has to be good enough that the team on the hook for it agrees with the number. Attribution and forecasting Connecting what the fleet costs to what the business is doing with it. Reconciling CSP billing exports against vendor telemetry and internal systems with mismatched schemas, attributing spend to the workloads and teams that generate it, and turning inference demand signals and research roadmaps into a defensible compute plan. Efficiency metrics have to survive contact with finance: stripped of pure demand and unit-price effects, reproducible month over month, and legible to a CFO. Key responsibilities Build the planning and allocation stack — the tools leadership uses to allocate capacity, teams use to plan against their allocations, and the scheduler enforces. Cross-region and cross-provider placement, guardrails, queueing, occupancy KPIs. Drive the efficiency programs: stranding and rightsizing, unused capacity recovery, and job-level utilization across training, inference, and eval. Establish per-config baselines and work with system-owning teams to close the gaps. Utilization improvements are worth enormous sums at our scale. Own attribution and forecasting — reconcile billing across ten-plus providers against telemetry and internal systems, attribute spend to the workloads that generate it, and turn demand signals and research roadmaps into a defensible compute plan and supply pipeline. Build the data platform underneath all of it: pipelines ingesting occupancy, utilization, and cost from a rapidly diversifying fleet into BigQuery, with real ownership of completeness, latency SLOs, and gap detection. Every new provider is a net-new integration. Operate Kubernetes-native systems at scale — collection agents, workload labeling, and the taint/reservation/scheduling behavior that determines what capacity is actually usable. Treat the output as a product, not a pipeline. Gather your own requirements, define schema contracts, and design for consumers ranging from research engineers to a CFO — including on-call and SLOs, because these surfaces are load-bearing for the company. What you bring A strong track record building and operating production systems. This is a hands-on engineering role with a devops flavor. Python and SQL at production quality. Most pipeline code is Python; the presentation layer is BigQuery SQL, including table-valued functions and views. Both need to be idiomatic, well-tested, and maintainable. Deep experience with at least one major cloud provider (Amazon Web Services, Google Cloud, or Microsoft Azure) and its operations Experience with observability tooling stack, including Prometheus, PromQL, and Grafana, including writing recording rules and building monitoring that engineering teams rely on. Ability to gather your own requirements and work across organizational boundaries in an ambiguous environment with limited direction. Preferred qualifications Experience with capacity planning, resource management, or cost attribution systems at a hyperscaler or in a large-scale machine learning environment. Time spent in product engineering and developer experience absolutely counts here. Scheduling and packing efficiency experience, or profiling-driven optimization of large distributed workloads. Multi-cloud data ingestion experience, especially normalizing billing exports, reservation APIs, on-demand capacity reservations, commitments, and vendor telemetry from providers with different billing arrangements. Total cost of ownership and forecasting experience, including decomposing whether infrastructure growth is causal or correlated with business drivers. Accelerator infrastructure familiarity. GPU metrics (DCGM), TPU utilization, Trainium power and utilization metrics, or experience with machine learning training and inference systems at the hardware level. Experience building internal data products with self-service access, schema contracts, API serving, documentation, and discoverability. Not just pipelines, but thinking about how the data gets consumed. Storage efficiency, retention, and lifecycle program experience at exabyte scale. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: New York City, NY 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 Anthropic manages one of the largest and fastest-growing infrastructure fleets in the industry — spanning multiple accelerator families, cpu families and clouds. The Capacity Engineering team is responsible for making sure all our infrastructure resources are accounted for, well-utilized, and efficiently allocated. We own the data, tooling, and operational systems that let Anthropic plan, measure, and maximize utilization across first-party and third-party compute. As an engineer on Capacity Engineering, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on. You will be expected to write production-quality code every day, operate alongside Kubernetes-native infrastructure at meaningful scale, and directly influence decisions around one of Anthropic’s largest areas of spend. You’ll collaborate closely with research engineering, infrastructure, inference, and finance teams. The work requires someone who can move between data engineering, systems engineering, and observability with comfort — and who thrives in a high-autonomy, high-ambiguity environment. This is a pipeline role feeding four areas. Depending on your background and business priority, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap: Data platform Pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, normalize billing and usage across cloud providers, and serve the BigQuery tables the rest of the org queries against. Correctness, completeness, and latency are the job, not a footnote. Consumers range from research engineers to finance to leadership, so it's product work as much as engineering: defining schema contracts, making data discoverable, and figuring out what people actually need. Planning Knowing what the fleet has, where it's going, and what's in the way. Making the state of the fleet legible and actionable in real time: cluster health tooling, capacity planning platforms, alerting on occupancy drops and allocation problems, and systemic fixes to scheduling and fragmentation. Kubernetes operations on one side, cross-team coordination on the other. Efficiency Measuring and improving how effectively every major workload uses the hardware it runs on. Instrumenting utilization across training, inference, and eval systems, building benchmarking infrastructure, establishing per-config baselines, and working directly with system-owning teams to close the gaps. The metric has to be good enough that the team on the hook for it agrees with the number. Attribution and forecasting Connecting what the fleet costs to what the business is doing with it. Reconciling CSP billing exports against vendor telemetry and internal systems with mismatched schemas, attributing spend to the workloads and teams that generate it, and turning inference demand signals and research roadmaps into a defensible compute plan. Efficiency metrics have to survive contact with finance: stripped of pure demand and unit-price effects, reproducible month over month, and legible to a CFO. Key responsibilities Build the planning and allocation stack — the tools leadership uses to allocate capacity, teams use to plan against their allocations, and the scheduler enforces. Cross-region and cross-provider placement, guardrails, queueing, occupancy KPIs. Drive the efficiency programs: stranding and rightsizing, unused capacity recovery, and job-level utilization across training, inference, and eval. Establish per-config baselines and work with system-owning teams to close the gaps. Utilization improvements are worth enormous sums at our scale. Own attribution and forecasting — reconcile billing across ten-plus providers against telemetry and internal systems, attribute spend to the workloads that generate it, and turn demand signals and research roadmaps into a defensible compute plan and supply pipeline. Build the data platform underneath all of it: pipelines ingesting occupancy, utilization, and cost from a rapidly diversifying fleet into BigQuery, with real ownership of completeness, latency SLOs, and gap detection. Every new provider is a net-new integration. Operate Kubernetes-native systems at scale — collection agents, workload labeling, and the taint/reservation/scheduling behavior that determines what capacity is actually usable. Treat the output as a product, not a pipeline. Gather your own requirements, define schema contracts, and design for consumers ranging from research engineers to a CFO — including on-call and SLOs, because these surfaces are load-bearing for the company. What you bring A strong track record building and operating production systems. This is a hands-on engineering role with a devops flavor. Python and SQL at production quality. Most pipeline code is Python; the presentation layer is BigQuery SQL, including table-valued functions and views. Both need to be idiomatic, well-tested, and maintainable. Deep experience with at least one major cloud provider (Amazon Web Services, Google Cloud, or Microsoft Azure) and its operations Experience with observability tooling stack, including Prometheus, PromQL, and Grafana, including writing recording rules and building monitoring that engineering teams rely on. Ability to gather your own requirements and work across organizational boundaries in an ambiguous environment with limited direction. Preferred qualifications Experience with capacity planning, resource management, or cost attribution systems at a hyperscaler or in a large-scale machine learning environment. Time spent in product engineering and developer experience absolutely counts here. Scheduling and packing efficiency experience, or profiling-driven optimization of large distributed workloads. Multi-cloud data ingestion experience, especially normalizing billing exports, reservation APIs, on-demand capacity reservations, commitments, and vendor telemetry from providers with different billing arrangements. Total cost of ownership and forecasting experience, including decomposing whether infrastructure growth is causal or correlated with business drivers. Accelerator infrastructure familiarity. GPU metrics (DCGM), TPU utilization, Trainium power and utilization metrics, or experience with machine learning training and inference systems at the hardware level. Experience building internal data products with self-service access, schema contracts, API serving, documentation, and discoverability. Not just pipelines, but thinking about how the data gets consumed. Storage efficiency, retention, and lifecycle program experience at exabyte scale. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: Kazakhstan FREEDOM FINANCE TECHNOLOGIES is a Cyprus-based fintech company and part of the international financial holding Freedom Finance. We develop and maintain trading and banking IT products, as well as the holding’s IT infrastructure worldwide. Our team currently consists of 170 professionals.  Over the past three years, we have grown fourfold and continue to actively expand our team. Since 2009, we have been developing our core product - a trading system for brokerage operations in the stock market. More than 700,000 users across 41 countries use our applications. We are expanding our IT Infrastructure team and are looking for a Network Engineer to support, develop, and modernize our corporate office network. Our office network is built around a centralized FortiGate core located in the data center and managed by a dedicated team. At the office level, we primarily use MikroTik and Ubiquiti equipment. We are looking for someone who can bring consistency and order to the network infrastructure by improving segmentation, implementing high availability, automating monitoring, and keeping documentation up to date. Key Responsibilities: Network Operations & Development Administer and develop the office network infrastructure (primarily MikroTik, with Ubiquiti deployed in several locations). Network Optimization Reconfigure network segmentation across all office locations, optimize the existing infrastructure, and eliminate bottlenecks. High Availability Design and implement high-availability solutions for network equipment and WAN connectivity. Monitoring & Automation Deploy network monitoring systems and introduce additional services to simplify network administration. Documentation & IP Address Management (IPAM/Wiki) Maintain and update the corporate knowledge base (Wiki), keep network diagrams for major office locations current, and manage the organization's IP address space (IPAM). New Office Deployments Remotely prepare and configure network equipment for new office openings. Collaboration Work closely with Internet service providers (ISPs) and the team responsible for the data center network core. Troubleshooting Diagnose and resolve incidents related to network infrastructure and associated services. Requirements Strong MikroTik Expertise Hands-on experience configuring, routing, and troubleshooting MikroTik RouterOS environments. Experience with Ubiquiti Solid understanding of configuring and managing UniFi and Edge product lines. Strong Networking Fundamentals In-depth knowledge of TCP/IP, the OSI model, routing, VLANs, VPNs, firewalls, and NAT. Practical experience designing and supporting high-availability environments, including link redundancy and load balancing. Linux Administration Solid experience with Ubuntu Linux, including network service configuration and basic server administration. Documentation Skills Experience with IP address management solutions (NetBox, phpIPAM, or similar) and maintaining network documentation and diagrams (Confluence, Notion, draw.io, Visio). Monitoring Systems Experience deploying, configuring, and customizing monitoring platforms such as Zabbix, Prometheus/Grafana, Nagios, or equivalent solutions. English — B2 (Upper-Intermediate) or higher Ability to communicate effectively in written English with external vendors and colleagues. Most day-to-day communication is conducted in English. Self-Driven & Organized Ability to work independently, take a structured approach to problem-solving, and maintain well-organized infrastructure and documentation. Excellent written and verbal Russian is essential for this role Nice to Have Experience with IP telephony, including Asterisk administration and configuration. Relevant certifications such as MTCNA, MTCRE, CCNA, or equivalent. Understanding of FortiGate architecture and operation to collaborate effectively with the data center networking team. Basic automation skills using Bash, Python, or Ansible to streamline routine operational tasks. Benefits Fully remote work. Daytime and evening working hours according to the Moscow time zone. A service contract with a Cyprus-based company. Fixed monthly salary in euros. Paid annual leave of 21 working days. Paid sick leave. Public holidays: New Year holidays until January 4–6, plus May 1–2 or May 8–9. One additional paid day off per month. Coverage of professional training courses related to your area of work. Coverage of participation in professional online conferences. Training on products and processes. A genuinely comfortable and professional working environment. Opportunities to grow and develop alongside the company and its products.

Location: Schaffhausen, Switzerland D-ploy is an IT and Engineering Solutions company with operations throughout the EMEA region including Switzerland, Germany, Czech Republic, Austria, UK, as well as the USA. We pride ourselves on delivering innovative and superior services and solutions to numerous industry-leading clients. By building relationships and trusted partnerships within the IT community, we optimize our customer‘s IT productivity and contribute to the organization’s success and value. We are interested in talking to engaging, flexible, and solution-oriented individuals who are looking to become a part of a dynamically growing and international organization. We are focused on creating value where IT counts, join us! Tasks and Responsibilities Monitor the performance of the company's desktop infrastructure and make suggestions for improving efficiency Act as the main point of contact for employees who are experiencing computer issues Ongoing maintenance and future planning needs of the company's desktop infrastructure Complex IT support tasks for our customer both onsite and remote maintenance (PC, laptop and peripherals) Regular front desk assignments for direct support in case of inquiries and problems of our customer Managing the inventory system (purchasing, goods receipt, maintenance and proper disposal) Carrying out simple installations in the infrastructure area (in particular network cabling) Setting up and providing the IT infrastructure for new employees Coordinate and plan PC installations, e.g. for workstation relocations, in cooperation with the existing team Processing of tickets in the ticket system and their documentation Evaluate applications and software patches for desktop applications to see if they will solve desktop issues Resolve software, hardware and device issues reported by the end users Installation, configuration and ongoing usability of desktop hardware and software IT support relating to technical issues involving Connect client Support services for Client standard technologies including basic troubleshooting of Exchange and adds/moves/changes for AD accounts Troubleshoot PC connectivity issues and network elements including ISP circuits, firewalls, network switches, wireless equipment and telephone system Remote access solution (VPN) implementation and support Document and update the local client environment Escalate service issues when required Communication with customers/end users as required, keeping them informed of incident progress, notifying them of impending changes or agreed outages Advise and train staff on computer operations and software applications Requirements Good understanding of Information Technology; trends, products and solutions 1+ years’ experience in a similar position supporting a variety of technologies and services In depth knowledge of: Windows OS (Windows 7 & 10) Microsoft Office & Office 365 applications PC\Laptop hardware PC\Laptop peripherals, including printers Mobile devices Basic knowledge of Active Directory Exchange ITIL certification and familiarity with the Service Now ticketing system is an advantage GxP knowledge is desired Highly self-motivated and directed with a keen attention to detail Customer focused with a passion and drive for customer satisfaction and delivering business values Solid problem solving and analytical skills Ability to work independently as well as willing to follow directions and best practices Great communication and interpersonal skills Friendly, supportive and flexible personality with quick learning ability Good team player with planning and organizing skills & multi-task Fluent (at least B2) written and oral English and German language, French language is a plus Willing to travel within Schaffhausen area Drivers license and a private car Candidates have to declare Criminal record extract not older than 3 months Benefits Broad range of activities, tasks, and projects Lunch Checks or Reka Money (Lunch Checks can be used in 7000+ restaurants, Reka Money can be used for Transport & Accommodation,Sport & Wellbeing, Leisure &Culture, Holidays & Travel) Enjoy free refreshments in our D-ploy offices Insurance Benefits Gym Benefits Fishing for Friends program – our referral program Further development and professional advancement Friendly and international working environment Competitive salary and various benefits Is IT in your DNA? If you believe you are a good match for this role, or any other opportunities for which D-ploy is currently staffing, then we are very keen to speak with you. You can apply though a variety of channels, including our website www.d-ploy.ch or by reaching out directly to our recruitment team recruiting@d-ploy.ch .

Location: Remote - United States Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . At Reddit, machine learning sits at the heart of how millions of people discover, connect, and engage with the world’s largest collection of human conversations. From “How do I fix a leaky faucet?” to “What should I watch next?”, the Answers Relevance team leverages AI to connect people with the real-world wisdom of Reddit communities. We’re hiring Senior and Staff Machine Learning Engineers to make Answers smarter, more relevant, and more helpful. Our SOTA AI systems helps surface the right conversations, but the heart of Answers is still people helping people ~ Redditors sharing what they know with each other! What You’ll Work On You’ll lead ML projects from idea to production, partnering with Product, Data Science, and Engineering across recommendations, search, messaging, and AI systems. You don’t need experience in every area listed below. A background in a few may make you a great fit! Recommendations, search, content understanding, or large-scale data and ML infrastructure SOTA large scale RecSys Foundational retriever models RAG systems, retrieval methods, LLM evaluation, or production LLM features Agentic AI, such as tool calls, MCPs, multi-agent systems, or orchestration Self-hosted open-weight models such as Qwen or Llama; prompt engineering, fine-tuning, post-training, or prompt optimization Improving grounding, instruction following, and hallucination; evaluating retrieval and generation quality Scaling LLM systems, including latency, caching, and cost; safety measures for prompt injection and unsafe inputs Defining product success metrics, feedback loops, and evaluation approaches, including human review What You’ll Do Build and improve large-scale ML systems, including retrieval and ranking, embeddings, and LLM-powered features Set technical direction and deliver measurable user and business impact Work with partner teams to shape product and technical roadmaps Evaluate new AI/ML approaches and support responsible LLM development Mentor engineers and contribute to long-term ML planning Required Qualifications 4–12 years of experience building, deploying, and operating ML systems in production Broad knowledge of ML fundamentals and experience applying them in production; expert-level modeling depth isn’t required Experience developing and productionizing models with TensorFlow, PyTorch, or Hugging Face Transformers Experience writing production-quality code, including testing, evaluation, and monitoring; Python experience required, Golang experience a plus Experience with ML systems such as data pipelines, feature engineering, model training and serving, and production monitoring Strong communication and collaboration skills, and a record of delivering measurable product impact Benefits Comprehensive Healthcare Benefits and Income Replacement Programs 401k with Employer Match Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support Family Planning Support Gender-Affirming Care Mental Health & Coaching Benefits Flexible Vacation & Paid Volunteer Time Off Generous Paid Parental Leave #LI-Remote Pay Transparency: This job posting may span more than one career level. In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/ . To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below. The base salary range for this position is: $216,700 — $322,000 USD In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews. During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable.  We will not sell your personal information or disclose it to any third party for their marketing purposes.  We will delete any recording of your interview promptly after making a hiring decision.  For more information about how we will handle your personal information, including our retention of it, please refer to our Candidate Privacy Policy for Potential Employees and Contractors . Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve.  Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.

Location: San Francisco, CA 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 Anthropic manages one of the largest and fastest-growing infrastructure fleets in the industry — spanning multiple accelerator families, cpu families and clouds. The Capacity Engineering team is responsible for making sure all our infrastructure resources are accounted for, well-utilized, and efficiently allocated. We own the data, tooling, and operational systems that let Anthropic plan, measure, and maximize utilization across first-party and third-party compute. As an engineer on Capacity Engineering, you will build the production systems that power this work: data pipelines that ingest and normalize telemetry from heterogeneous cloud environments, observability tooling that gives the org real-time visibility into fleet health, and performance instrumentation that measures how efficiently every major workload uses the hardware it’s running on. You will be expected to write production-quality code every day, operate alongside Kubernetes-native infrastructure at meaningful scale, and directly influence decisions around one of Anthropic’s largest areas of spend. You’ll collaborate closely with research engineering, infrastructure, inference, and finance teams. The work requires someone who can move between data engineering, systems engineering, and observability with comfort — and who thrives in a high-autonomy, high-ambiguity environment. This is a pipeline role feeding four areas. Depending on your background and business priority, you’ll focus primarily in one, but the boundaries are fluid and the problems overlap: Data platform Pipelines that ingest occupancy and utilization telemetry from Kubernetes clusters, normalize billing and usage across cloud providers, and serve the BigQuery tables the rest of the org queries against. Correctness, completeness, and latency are the job, not a footnote. Consumers range from research engineers to finance to leadership, so it's product work as much as engineering: defining schema contracts, making data discoverable, and figuring out what people actually need. Planning Knowing what the fleet has, where it's going, and what's in the way. Making the state of the fleet legible and actionable in real time: cluster health tooling, capacity planning platforms, alerting on occupancy drops and allocation problems, and systemic fixes to scheduling and fragmentation. Kubernetes operations on one side, cross-team coordination on the other. Efficiency Measuring and improving how effectively every major workload uses the hardware it runs on. Instrumenting utilization across training, inference, and eval systems, building benchmarking infrastructure, establishing per-config baselines, and working directly with system-owning teams to close the gaps. The metric has to be good enough that the team on the hook for it agrees with the number. Attribution and forecasting Connecting what the fleet costs to what the business is doing with it. Reconciling CSP billing exports against vendor telemetry and internal systems with mismatched schemas, attributing spend to the workloads and teams that generate it, and turning inference demand signals and research roadmaps into a defensible compute plan. Efficiency metrics have to survive contact with finance: stripped of pure demand and unit-price effects, reproducible month over month, and legible to a CFO. Key responsibilities Build the planning and allocation stack — the tools leadership uses to allocate capacity, teams use to plan against their allocations, and the scheduler enforces. Cross-region and cross-provider placement, guardrails, queueing, occupancy KPIs. Drive the efficiency programs: stranding and rightsizing, unused capacity recovery, and job-level utilization across training, inference, and eval. Establish per-config baselines and work with system-owning teams to close the gaps. Utilization improvements are worth enormous sums at our scale. Own attribution and forecasting — reconcile billing across ten-plus providers against telemetry and internal systems, attribute spend to the workloads that generate it, and turn demand signals and research roadmaps into a defensible compute plan and supply pipeline. Build the data platform underneath all of it: pipelines ingesting occupancy, utilization, and cost from a rapidly diversifying fleet into BigQuery, with real ownership of completeness, latency SLOs, and gap detection. Every new provider is a net-new integration. Operate Kubernetes-native systems at scale — collection agents, workload labeling, and the taint/reservation/scheduling behavior that determines what capacity is actually usable. Treat the output as a product, not a pipeline. Gather your own requirements, define schema contracts, and design for consumers ranging from research engineers to a CFO — including on-call and SLOs, because these surfaces are load-bearing for the company. What you bring A strong track record building and operating production systems. This is a hands-on engineering role with a devops flavor. Python and SQL at production quality. Most pipeline code is Python; the presentation layer is BigQuery SQL, including table-valued functions and views. Both need to be idiomatic, well-tested, and maintainable. Deep experience with at least one major cloud provider (Amazon Web Services, Google Cloud, or Microsoft Azure) and its operations Experience with observability tooling stack, including Prometheus, PromQL, and Grafana, including writing recording rules and building monitoring that engineering teams rely on. Ability to gather your own requirements and work across organizational boundaries in an ambiguous environment with limited direction. Preferred qualifications Experience with capacity planning, resource management, or cost attribution systems at a hyperscaler or in a large-scale machine learning environment. Time spent in product engineering and developer experience absolutely counts here. Scheduling and packing efficiency experience, or profiling-driven optimization of large distributed workloads. Multi-cloud data ingestion experience, especially normalizing billing exports, reservation APIs, on-demand capacity reservations, commitments, and vendor telemetry from providers with different billing arrangements. Total cost of ownership and forecasting experience, including decomposing whether infrastructure growth is causal or correlated with business drivers. Accelerator infrastructure familiarity. GPU metrics (DCGM), TPU utilization, Trainium power and utilization metrics, or experience with machine learning training and inference systems at the hardware level. Experience building internal data products with self-service access, schema contracts, API serving, documentation, and discoverability. Not just pipelines, but thinking about how the data gets consumed. Storage efficiency, retention, and lifecycle program experience at exabyte scale. The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

As the Engineering Manager for the AirCover Insurance Platform, you will lead the system that lets Airbnb launch, price, enroll and service insurance products in new regions, and that lets the business test what drives Hosts and guests to be covered. Depth in backend platforms, with experience in distributed systems and high-availability services.

Задачи: — Руководство проектированием, разработкой и внедрением on-premises MLOps решений для поддержки доставки моделей машинного обучения — Сотрудничество с дата-сайентистами и разработчиками для создания масштабируемых и эффективных конвейеров данных, систем обучения и деплоя моделей — Разработка и поддержка инструментов мониторинга и управления для обеспечения надежности и производительности MLOps инфраструктуры — Взаимодействие с заинтересованными сторонами для понимания их потребностей в машинном обучении и разработки соответствующих решений — Отслеживание новейших трендов и технологий в области MLOps, машинного обучения и искусственного интеллекта, обмен знаниями с командой — Наставничество младших членов команды и помощь в развитии их навыков в MLOps — Поиск и устранение проблем пользователей Требования: — Опыт работы 5+ лет в MLOps, разработке ПО, инженерии данных или смежных областях — Сильные навыки программирования на современных языках (Java, Scala, Python, Kotlin) — Практический опыт работы с Kubernetes: деплой и эксплуатация рабочих нагрузок, отладка сбоев, работа с основными концепциями (Deployments, ConfigMaps/Secrets, Services, resource limits) — Отличные коммуникативные и командные навыки — Ценность простоты и производительности кода Будет плюсом: — Понимание жизненного цикла ML-проектов — Опыт работы с MLOps платформами, такими как Kubeflow или MLFlow — Опыт с ML-фреймворками: scikit-learn, LGBM, Tensorflow, PyTorch — Опыт работы с Big Data инструментами: Spark, S3, Hadoop — Глубокие знания контейнеризации и оркестрации (Docker, Kubernetes) — Опыт DevOps и CI/CD практик — Опыт создания и масштабирования инструментов для обслуживания моделей, включая построение API с жесткими SLA — Страсть к инженерной стороне машинного обучения и масштабированию — Опыт проектирования и построения MLOps инфраструктуры, включая конвейеры данных, системы обучения и деплоя моделей, а также инструменты мониторинга и управления.

Location: Indianapolis, United States; Ohio City, United States; Chicago, United States About us: Shape the Future with AI, Ignite Your Potential Xenon7 is an inferno where skill, dedication and passion run together. About our client: Global healthcare leader headquartered in Indianapolis, Indiana. The Cardiometabolic Research (CMR) Therapeutic Area of our client, focuses on the discovery of biologic, small molecule and genetic therapeutics for the treatment of cardiometabolic diseases and associated complications. The Cloud and Connectivity organization is seeking an engineer in HPC platforms. This role will focus on enabling analytical, statistical, and scientific computing capabilities within high performance computing infrastructure. We are looking for an HPC Platform Engineer to help accelerate the next era of high performance computing innovation. What You’ll Be Doing You will be a leader the engineering and operations of design, build, and maintain scalable HPC platforms. You will play a crucial role in enabling HPC infrastructure and experiences. Collaborate with researchers and scientists to optimize performance and streamline workflows. Leverage tooling and automation for orchestration, resource scheduling, data access, and reproducibility. Evolve and operate public cloud and on-premises environments with a focus on availability and performance for HPC workloads. Define and monitor infrastructure metrics, resource utilization, among others. How You’ll Succeed Be Bold - You will bring a high learning agility and platform engineering skills to enable the Technology strategy, identifying opportunities to accelerate our HPC journey. Be Fast - You will advance initiatives to enable critical business projects. Be Proactive - You will have opportunities to leverage agile ways of working with a willingness to become an expert in deploying HPC solutions. Be Your Best - You will learn about new technologies in HPC. Requirements What You Should Bring Hands-on experience in HPC platforms, including knowledge of accelerators (e.g., GPU), HPC schedulers (e.g., Altair Grid Engine, Slurm), Kubernetes platforms, and containers technologies (Docker, Apptainer). Demonstrated experience in HPC workloads, infrastructure, and cluster architectures. Expertise with the Linux command line, Linux troubleshooting, and HPC administration. Experience with DevOps tools such as Github, Chef, Ansible, Terraform. Experience with automating infrastructure and applications. Passion for continual learning and staying informed of new technologies, infrastructure trends, and approaches in the HPC field. Strong programming and scripting skills in languages such as Python or Bash. Basic Requirements Bachelor’s degree in computer science, Information Technology, or related technical field. 5+ years’ experience HPC Platform Engineer. Demonstrated experience leading a global large-scale infrastructure project. Benefits Attractive, market-leading salary package. Clear career advancement path with professional development opportunities.