Вакансии

Разрабатывать и поддерживать CI-пайплайны: сборку компонентов, запуск автоматических проверок, публикацию артефактов и подготовку тестовых окружений; Автоматизировать создание, настройку, обновление и удаление стендов разработки и тестирования;

Создавать и поддерживать IaaS для множества собственных проектов компании на собственных ресурсах и арендуемых сервисах. Работать над повышением стабильности и отказоустойчивости инфраструктуры и инфраструктурных сервисов.

Стандартизации эксплуатационных и DevOps-практик, включая разработку типовых шаблонов CI/CD, Helm-чартов, конфигураций мониторинга, логирования и правил эксплуатации; Развития систем мониторинга и наблюдаемости (observability);

подготовка и поддержка окружений для разработки, тестирования и демонстрации решений; сборка, запуск и сопровождение приложений в Docker, актуализация Dockerfile и Docker Compose‑конфигураций;

Разработка и обучение ML/DL-моделей для задач NLP и Computer Vision. Возможен также classic ML.

Мы ищем аналитика, который будет полностью отвечать за внутреннюю отчётность: быстро собирать данные из разных систем, склеивать их в единый формат и передавать заказчикам в виде качественных отчётов, презентаций и финансовых моделей. Оперативно собирать отчётность по запросам внутренних заказчиков.

Сбор и обработка данных из различных источников (рекламные сети, продукты, системы атрибуции трафика). Автоматизация отчетности по трафику и монетизации приложений, составление требований к дашбордам.

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

развивать и сопровождать код подрядчика для управления автопарком роботов; выполнять задачи в рамках backend (Python/Django) и frontend (React/TypeScript);

Location: Москва Ищем опытного продуктового аналитика в команду, развивающую цифровые продукты автострахования в рамках нашей компании. Аналитик будет работать на стыке данных, бизнеса и IT: исследовать клиентский путь, находить точки роста, проверять продуктовые гипотезы, проектировать эксперименты и помогать команде принимать решения на основании данных. Обязанности * Проведение полного цикла продуктовых исследований: анализ клиентского пути, воронок, конверсий, сегментов и поведения клиентов для поиска точек роста и причин изменения метрик; * Формулирование, приоритизация и проверка гипотез; самостоятельное проведение ad-hoc исследований от постановки задачи до рекомендаций бизнесу; * Проектирование и анализ A/B-тестов и пилотов: расчет выборки и MDE, контроль сопоставимости групп, оценка uplift, статистической значимости и экономического эффекта; * Работа с большими массивами данных: оптимизация сложных SQL-запросов (Greenplum), объединение данных из нескольких источников, контроль качества расчетов; * Использование Python для статистического анализа, автоматизации расчетов и отчетности, прототипирования аналитических решений; * Развитие методологии и отчетности: определение принципов расчета метрик, автоматизация регулярных расчетов, создание дашбордов и визуализаций; * Оценка влияния продуктовых изменений на конверсию, продажи и финансовый результат; * Кросс-функциональное взаимодействие с PO, разработкой и DS: трансляция бизнес-вопросов в аналитические задачи и результатов анализа — в конкретные рекомендации; * Применение AI-инструментов для ускорения аналитической работы и автоматизации. Требования * Опыт работы по профильному направлению от 3 лет; * Твердые знания продуктовой аналитики: понимание продуктовых метрик и принципов их выбора под бизнес-задачу, методология экспериментального анализа; * Уверенное владение SQL и практический опыт решения сложных аналитических задач: оптимизация запросов, объединение данных из нескольких источников; * Практическое владение Python как рабочим инструментом: pandas, NumPy, Jupyter/DataLab, библиотеки статистического анализа и визуализации; * Понимание принципов экспериментального анализа: расчет выборки и MDE, контроль сопоставимости групп, оценка uplift и статистической значимости; * Опыт работы с большими объемами данных и промышленными СУБД (Greenplum / PostgreSQL); * Практический опыт анализа clickstream / event-based данных и пользовательских путей; * Опыт автоматизации аналитической отчетности и построения дашбордов; * Опыт работы в финтехе, страховании, e-commerce или другом цифровом продукте с большими данными; * Опыт работы с Process Mining или анализа клиентских процессов и последовательностей событий; * Законченное высшее образование по технической, экономической или аналитической специальности; * Инструментальное владение AI для анализа, генерации и автоматизации. Условия * Офисный формат работы (современный офис в Москве возле станции "Кутузовская") * Льготные ипотечные условия кредитования; * Подписка Прайм с возможностью совместного использования на трёх близких; * Скидки на продукты компаний – партнеров: Okko, Сбер Маркет, Мега Маркет, Самокат, Еаптека и другие; * ДМС с первого дня и скидки на страхование для близких; * Корпоративная пенсионная программа; * Детский отдых и подарки за счет Компании; * Обучение за счет Компании, Онлайн курсы, неограниченный доступ к библиотеке и обучение на базе Корпоративного университета, тренинги, митапы и возможность получить новую квалификацию.

• Проводить функциональное, регрессионное и смоук-тестирование; • Тестировать баланс, монетизацию, различные механики, вёрстку и игровую логику;

Чем предстоит заниматься: Проектирование и разработка новых микросервисов и бизнес-логики. Оптимизация производительности существующих компонентов системы, работа с высоконагруженными участками кода.

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. 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.

-поддерживать и улучшать рекомендательный сервис и CV наших продуктов. -участвовать в построении пайплайнов по сбору данных для последующего моделирования.

Role Overview The Strategic WFM Analyst sits in the Core Workforce Management (WFM) Planning & Operations Strategy team and is responsible for owning centralized forecasting, capacity planning, workforce cost optimization, and strategic headcount trade-offs across all lines of business (LOBs). This role is a high-judgment governance function that moves beyond traditional scheduling and intraday monitoring. It translates complex demand forecasts into defensible FTE, BPO, and overtime spend strategies, models capacity risks, and aligns executive stakeholders across Finance, Product, and Operations. You should be exceptionally strong in WFM maths and financial analytics, exhibit deep strategic curiosity, navigate messy raw data effortlessly, and communicate recommendations with executive-level clarity. What You'll Do: — Translate multi-LOB demand forecasts into monthly and quarterly capacity, headcount, and budget requirements. — Model workforce and financial trade-offs across internal FTEs, BPO partners, overtime spend, bonus hours, productivity shifts, and shrinkage. — Analyze demand-funnel changes (e.g., product feature rollouts, AI bot deflection shifts) and quantify their exact capacity and dollar impact. — Partner directly with Finance, Product, and Operations to validate baseline assumptions, enforce input SLAs, and align on staffing commitments. — Draft concise, decision-ready executive memos and Weekly Business Reviews (WBRs) for Directors and leadership. — Conduct deep-dive root-cause analyses whenever operational actuals deviate from capacity plans, and establish corrective action paths. — Standardize capacity planning frameworks, modeling templates, and operating standards across LOBs. — Maintain deep forecasting competency to audit models, challenge underlying assumptions, and provide seamless coverage across the broader central WFM engine. What We're Looking For: — Strategic WFM Maths: Exceptionally strong quantitative background with deep expertise in WFM fundamentals (capacity modelling, Erlang, shrinkage, AHT, SLA/ASA trade-offs) and advanced proficiency in Google Sheets, SQL, or Python. — Business & Financial Judgement: Demonstrated ability to connect operational queue dynamics directly to dollar exposure, hiring velocity, BPO costs, and SLA risk. — Executive Communication: Superior strategic writing skills with a natural instinct to distil complex, multi-variable workforce analytics into scannable, 1-2 page decision memos. — Cross-Functional Influence: Proven ability to establish governance frameworks, challenge bad assumptions with back-tested data, and align Finance, Product, and Operations leadership without formal authority. — Blank-Page Ownership: High comfort operating in ambiguous environments with incomplete data, isolating what materially matters without requiring step-by-step direction. WFM Analyst Profile: Strong core WFM domain expertise with a strategic/Ops spike. This is not a generalist role and not a passive, traditional scheduler role. Hiring Process: Application Diagnostic Case Study Case Study Interview - Technical & Capacity Executive Judgement & Strategic Interview Culture Interview (People Ops) Offer

Управлять командой аналитиков, вести техническое менторство, участвовать в найме, формировать и декомпозировать задачи; Много взаимодействовать с заказчиками в разных частях бизнеса;

Architect and tune customer and channel-partner deployments end-to-end — API integrations, third-party data integrations, SDKs, and latency constraints. Serve as Alloy's credible technical voice with customer engineering leadership, making architectural calls in real time.

As an Operations Engineer, you'll apply AI at the forefront of BizTech's operational health: using LLM-powered triage and intelligent automation to resolve tickets, speed up incident response, and build self-healing observability that catches problems before they escalate. AI fluency is core to this role, not an add-on.