Вакансии

Design and develop end-to-end data pipelines using Azure Data Factory, SQL, and other Azure services. Implement complex data models, DAX measures, and calculated columns to support reporting.

We are looking for a Senior Cloud Engineer specializing in Microsoft Azure on behalf of our client to design, automate, and maintain high-availability cloud infrastructure. Architect and deploy enterprise-grade Azure infrastructure using Terraform/Bicep, maintaining modular IaC libraries and strict state management workflows.

You’ll be responsible for building modern and scalable web applications, working closely with backend developers and designers to create intuitive user experiences.

Location: Москва Команда специализируется на разработке решений для анализа данных дистанционного зондирования Земли (ДЗЗ). Упор делается не только на применение state-of-the-art CV-решений и больших мультимодальных LLM, но и моделей, которые могут быть эффективно использованы на edge-устройствах. Наши проекты включают работу с ДЗЗ для задач экологии, урбанистики, безопасности, автономной навигации. Обязанности * планирование и координация работы команды * коммуникация с внешними командами, анализ требований на разработку моделей и выработка плана решения * разработка моделей компьютерного зрения для обработки данных ДЗЗ (спутниковые снимки, инфракрасная съемка, мультиспектральные данные, ортофотопланы, геопривязанные видео, геопривязанные фото). Задачи: локализация, детекция, семантическая сегментация, детекция изменений, регистрация изображений * оптимизация моделей для промышленного использования и использования на edge-устройствах * взаимодействие с командой RUN для передачи алгоритмов в production * документирование экспериментов, результатов и процессов (MLflow, Tensorboard). Требования * опыт работы в Data Science / CV — от 5 лет * хорошие инженерные навыки, понимание общего процесса ИТ-разработки и DevOps процессов * хорошее понимание основ классического компьютерного зрения: модель камеры, матрицы гоммографии, ключевые точки и SFM-реконструкция, фильтры и морфология, PnP и др * глубокое знание Python и стеков для ML/CV: OpenCV, PyTorch * Знание и понимание основных архитектур моделей для решения CV задач * опыт работы с алгоритмами локализации (SLAM, Kalman Filter, Particle Filter, PGO и др.) и алгоритмами регистрации изображений (SIFT, SuperGlue, SuperPoint, Roma, MINIMA-вариации) * опыт работы с робототехническими данными ROS/ROS2, работа с сенсорами (лидары, IMU, GPS) * умение читать научные статьи (в основном на английском) и быстро тестировать новые подходы Будет плюсом: * публикации или выступления на конференциях (по задачам робототехники, компьютерного зрения или локализации) * знание и понимание методов sensor fusion * работал ранее с симуляторами (airsim, flightmare, CARLA, Isaac Sim) * навыки работы с геоинформационными системами (GIS): QGIS. Условия * комфортный современный офис рядом с м. Кутузовская * гибридный формат работы * ежегодный пересмотр зарплаты. Годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * программа адаптации и помощь руководителя на старте * 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.

Location: Москва Мы — трайб «Корпоративное взыскание» Сбербанка. Наш продукт –совокупность сервисов, упрощающих всю работу по взысканию: от поступления информации о проблемах у клиента до успешного урегулирования его задолженности. Работаем по agile, используем Self-Contained Systems архитектуру, пишем много тестов, развиваем DevOps. Обязанности * сопровождать автоматизированные системы: чинить, если что-то сломалось; поднимать, если что-то упало; развивать систему мониторинга, чтобы до пользователей узнавать о том, что что-то упало; придумывать обходные пути до устранения проблемы разработчиками; расставлять костыли, чтобы больше не падало * внедрять новые релизы: проверять на тестовом стенде, что хуже не стало; установить на ПРОМ и убедиться, что всё работает * организовывать приёмо-сдаточные испытания нового релиза: поддерживать тестовый стенд в актуальном состоянии, договариваться со смежными подразделениями об интеграционных проверках; фиксировать выполненные проверки и полученные договорённости * автоматизировать рутинные задачи * использовать и адаптировать AI-инструменты для повседневных задач * получать удовольствие от работы, непрерывно расти и развиваться как IT-специалист, отращивать себе T-shape. Требования * высшее профильное техническое образование * опыт работы во 2-й линии сопровождения (L2/L3) от 2 лет * уверенные знания Linux, опыт работы в командной строке, решения типовых проблем * навыки администрирования СУБД (Oracle/MsSQL/PostgreSQL) * опыт работы с Docker/Podman/K8s/Openshift * знакомство с различными системами мониторинга * опыт использования инструментов DevOps * умение писать скрипты на Bash/Python * уверенные знания ITIL/ITSM, знание основных понятий и процессов * знакомство с моделями жизненного цикла разработки ПО (waterfall, agile, и т.д.) понимание принципов работы компьютера, основы computer science знание SQL на уровне сложных запросов * опыт использования GigaChat, Kandinsky и аналогов в продуктах, навыки создания и использования AI-агентов. Условия * комфортный современный офис г. Москва, Кутузовский проспект 32 * гибридный формат работы * ежегодный пересмотр зарплаты, годовая премия * корпоративный спортзал и зоны отдыха * система обучения для профессионального и карьерного развития * расширенный полис ДМС с первого дня работы и страхование для семьи * программа ипотеки для сотрудников * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера.

В команду по производству напольных покрытий ищем Программиста 1С:ERP для развития функционала существующей инсталляции 1С:ERP. Доработки и развитие базовых модулей ERP (БУНУ, МСФО, закупки, продажи, склад, производство);

Location: Москва Наша команда занимается развитием налоговых и бухгалтерских сервисов для корпоративных клиентов в Цифровом Корпоративном Банке "СберБизнес" в каналах Web и Mobile. Наша месячная аудитория насчитывает порядка 350 тыс. индивидуальных предпринимателей и организаций. Обязанности * участие в архитектурном обсуждении и проработке решений * разработка и поддержка сервисов * оптимизация производительности * Code Review * CI/CD. Требования * опыт от 5 лет разработки высоконагруженных и отказоустойчивых систем * уверенное знание Java 21+ * понимание микросервисной архитектуры, распределенных транзакций * глубокое знание Spring Framework, Spring Boot  * Unit-тесты, знание фреймворков JUnit, Mockito * разработка REST-сервисов, проектирование API-контрактов * понимание внутреннего устройства PostgreSQL (индексы, планировщик запросов, партиционирование), опыт работы с миграциями (Liquibase) * практический опыт работы с Kafka * практический опыт работы с Docker / Kubernetes (Helm, probes, Service Mesh — опционально) * понимание принципов DevOps и CI/CD * настройка Observability: распределенная трассировка (OpenTelemetry, Jaeger), метрики (Prometheus), логирование (ELK/EFK). * понимание базовых метрик нагрузочного тестирования и умение интерпретировать их для оптимизации производительности системы * базовые навыки работы в Unix-системах. Будет плюсом: * использование ИИ-агентов для ускорения разработки * практический опыт применения методологии Spec-Driven Development. Условия * комфортный современный офис рядом с м. Кутузовская, ул. Кутузовский проспект 32, формат работы - гибрид * 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 * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера.

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.

проектирование, разработка и поддержка высоконагруженных backend-систем; сопровождение и мониторинг работы сервисов в продуктивной среде;

Тестировать функциональность продукта на веб и ТВ-платформах: от главной страницы и плеера до личного кабинета. Проверять сценарии подписки и платежей: оформление, автопродление, отмена, возвраты, граничные состояния.

Сопровождение ML-решений (мониторинг сервисов и моделей, отслеживание дрейфа данных, работа над развитием); Оценка влияния на бизнес предложенных решений;

Задачи: Вести людей и команду: регулярные 1:1, развитие, обратная связь, рост инженерной планки; Планировать delivery вместе с продуктом/аналитикой: приоритеты, декомпозиция, оценки, риски, прогнозируемость;

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.

-Разработка Web-интерфейса Агента AI PDLC. -Участие в принятии архитектурных решений.

· Эксплуатация PostgreSQL: администрирование production-баз данных, настройка конфигурации, мониторинг производительности и ресурсов, диагностика и устранение проблем с подключениями, блокировками, транзакциями и обслуживанием данных.

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.

Обязанности: Разрабатывать новый функционал, модули и компоненты внутренних бизнес-приложений на 1C Bitrix/Bitrix24 и Laravel. Проектировать высоконагруженные интеграции с внутренними и внешними сервисами (REST, SOAP, очереди).