Вакансии

Location: United States Workplace: Remote The Connectors team is the bridge between ClickHouse and the broader ecosystem. We build and maintain the integrations that make ClickHouse accessible to millions of developers, data practitioners, and AI agents worldwide, from high-level data visualization plugins (Tableau, PowerBI, Superset, Metabase) to connectors for data frameworks (Apache Spark, Flink, Kafka Connect, Fivetran), orchestration platforms, and AI tooling. Our work directly shapes how companies process massive datasets: real-time analytics platforms that ingest millions of events per second, observability systems that monitor global infrastructure, and, increasingly, AI-powered data applications that redefine how teams work with data. We collaborate closely with the open-source community, internal teams, and enterprise users to ensure ClickHouse integrations set the standard for performance, reliability, and developer experience. ABOUT THE ROLE As a Senior Software Engineer specializing in the AI & ML ecosystem, you'll be a core contributor owning and evolving critical parts of ClickHouse's AI ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Engineers and Data Scientists to harness ClickHouse's speed and scale in the frameworks they already use. We're looking for someone who has firsthand experience as an AI & MLEngineer or Data Scientist. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why. You'll own the full lifecycle of key AI/ML integrations, driving architecture, performance, and feature direction across AI & LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with ClickHouse as a vector store, ML feature stores, and LLM-powered data applications. ClickHouse's columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real:  building the robust, production-ready connectors that make ClickHouse the natural choice when data practitioners design their next-generation AI and data systems. WHAT YOU'LL DO - Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design - Drive the AI/LLM integration strategy:  designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications - Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback - Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities - Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows ABOUT YOU - 7+ years of software development experience, including hands-on time as a Data Scientist or ML Engineer - Deep, proven experience designing, building, and maintaining production-grade Python connectors, SDKs, or integrations for at least one major platform (orchestration, BI, MLOps, or data transformation) - Hands-on experience applying AI/ML in production data-engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling that shipped and ran in production - Solid experience with the Python data ecosystem: Pandas, NumPy, Pydantic, and related libraries - Strong database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases - Solid experience with concurrent Python: threading, multiprocessing, and async patterns - Outstanding written and verbal communication; comfortable collaborating across engineering functions and with open-source communities Bonus points for: - Prior experience as a Data Engineer or Data Scientist in a product-facing or platform role - Familiarity with ClickHouse or similar high-performance OLAP platforms - Familiarity with the JVM ecosystem - Experience deploying AI/ML models in production, including inference APIs and vector databases - Familiarity with tools like dbt, Airflow, Dagster, Prefect is a plus PERKS - Flexible work environment - ClickHouse is a globally distributed company and remote-friendly. We currently operate in over 25 countries. - Healthcare - Employer contributions towards your healthcare. - Equity in the company - Every new eligible team member who joins our company receives equity. - Time off - Flexible time off in the US, generous entitlement in other countries. - A USD$500 Home office setup if you’re a remote employee. - Global Gatherings – We believe in the power of in-person connection and offer opportunities to engage with colleagues at company-wide offsites. CULTURE - WE ALL SHAPE IT As part of a rapidly scaling start-up, you will be instrumental in shaping our culture. Are you interested in finding out more about our culture? Learn more about our values here https://clickhouse.com/company/careers. Check out our blog posts https://clickhouse.com/blog or follow us on LinkedIn https://www.linkedin.com/company/clickhouseinc/life/0c1ee7f4-2129-488d-b413-e7f6270659fd/?viewAsMember=true to find out more about what’s happening at ClickHouse. EQUAL OPPORTUNITY & PRIVACY ClickHouse provides equal employment opportunities to all employees and applicants and prohibits discrimination and harassment of any type based on factors such as race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Please see here https://clickhouse.com/legal/applicant-privacy-notice for our Privacy Statement.

Location: Limassol, Cyprus Workplace: on-site Mayflower is a technology company building highload products used by millions of people worldwide. Operating at the scale of one of the world's top-50 websites, we solve complex engineering challenges and create solutions that power real-time entertainment for a global audience. We are looking for a Backend Team Lead for our ML development team focused on building and delivering production-grade ML-powered applications . This role is not about research or experimentation. You will be responsible for turning ML models into reliable, scalable, user-facing services . You will work at the intersection of machine learning and backend engineering , owning the full lifecycle of ML-powered features — from integration to deployment and operation in production. Job Responsibilities Design and build production ML services (APIs, microservices, real-time systems) Lead and grow a team of backend engineers : plan work, mentor team members, conduct code reviews, provide technical guidance, and foster engineering excellence. Drive architectural decisions and ensure the scalability, reliability, and maintainability of ML services. Integrate ML models into user-facing applications Ensure reliability, scalability, and performance of ML-powered systems Define and implement best practices for serving, versioning, and monitoring models in production Collaborate closely with: ML engineers / data scientists (who develop models) DevOps / MLOps team (who provide platform and infrastructure) Own the delivery pipeline of ML features into production Lead and mentor engineers working on ML-powered applications Drive architectural decisions around low-latency and high-load systems Identify bottlenecks between experimentation and production and eliminate them You’ll thrive here if you have 5+ years of experience in backend/software engineering in a Lead or Senior role (mentoring, ownership, decision-making). Strong commercial Python experience. Managerial experience leading an engineering team. Experience designing and building production backend systems and microservice architecture. Strong understanding of system design, scalability and high-load systems. Experience developing APIs (FastAPI, Flask, gRPC or similar). Hands-on experience with code reviews, mentoring and technical decision-making. Ability to drive cross-team collaboration and own complex technical integrations. That can be a plus: Experience with real-time ML systems (recommendations, ranking, personalization) Experience in video streaming or high-throughput platforms Familiarity with Kubernetes and cloud/on-prem environments Experience with feature stores, model monitoring, A/B testing Experience working alongside MLOps platforms (MLflow, Airflow, etc.) Conditions We know that great talent deserves great conditions, so here's what you can expect when joining us: EU-based employment contract and a 3-year Cyprus work visa with full support for your relocation and visa processes, including assistance for your family. EU passport. Opportunity to obtain an EU passport after 4 years of residence Full relocation package: flights to Limassol for you and your family, a company-covered apartment for the first month, and full relocation support to make your move smooth and hassle-free. Transparent performance reviews twice a year, with bonus opportunities and salary adjustments. Private medical insurance for you and your family Provident fund (Cypus): a long-term savings plan co-funded by you and the Company together (available after probation) that grows throughout your time with us in Cyprus. Corporate mobile plan (unlimited in Cyprus with roaming included) Mindfulness & well-being support, including psychological assistance with 50% coverage. 50% coverage of school and kindergarten fees for your children. Fully covered sports benefits, and also access to in-house electric scooters and bike rentals Professional growth support: language courses, conferences, training programs, and coaching. Peer recognition program: receive and share kudos for great work. A fully equipped office in Limassol’s city center, with everything you need for deep work and collaboration. Free breakfasts and lunches in the office and an in-house coffee bar with high-quality drinks and a health bar stocked with nutritious snacks. A strong engineering culture: international teams, corporate events, team buildings, and hackathons — because great work happens in great communities. Interview steps: 1. HR Interview (45 min) 2. Technical Interview (90 min) 3. Live code-review interview (90 min) 4. Final interview with the team (60 min)

Location: Ukraine; Moldova; Latvia; Slovakia; Czech Republic; Slovenia; Romania; Kazakhstan; Poland; Hungary; Montenegro; Estonia; European Union; Cyprus; Lithuania; Greece Workplace: Remote ABOUT US Ruby Labs is a leading tech company that creates and operates innovative consumer products. We offer a diverse range of opportunities across the health, education, and entertainment industries. Our innovative teams are driving the future of consumer-led products, and we're always looking for passionate individuals to join us. Learn more about our story at: https://rubylabs.com/about-us/ ABOUT THE ROLE We’re looking for a Lead Billing Data Analyst (Billing, Payments & Subscription Analytics) to own the data strategy behind our subscription billing operations. This is a hands-on leadership role: you’ll build analyses and pipelines that improve billing performance, while also managing and mentoring a team of analysts. KEY RESPONSIBILITIES ANALYTICS & TECHNICAL WORK - Monitor, analyze, and report on billing and support performance metrics (payments, renewals, refunds, disputes/chargebacks), and surface emerging trends. - Build and maintain data pipelines that deliver accurate, timely, and reliable billing data. - Apply statistical methods to model scenarios, run “what-if” analyses, and anticipate financial risk. - Design and run A/B tests and experiments to evaluate billing and support initiatives. - Analyze the impact of product, marketing, and pricing changes on billing outcomes, risk behavior, and customer support metrics. - Build and maintain dashboards and visual reporting that provide clear, real-time visibility into billing health and support performance. - Turn insights into recommendations that reduce disputes, churn, and operational inefficiencies. - Document analyses, workflows, definitions, and data logic to ensure clarity, reproducibility, and consistency. LEADERSHIP & COLLABORATION - Lead, mentor, and grow a team of data analysts: set expectations, coach on methodology, and ensure high-quality output. - Own the team’s intake and prioritization process; manage task flow and delivery timelines against business needs. - Uplevel the team’s billing domain knowledge and analytical/technical skills. - Partner cross-functionally with Billing, Support, Finance, Product, and Marketing to align metrics, definitions, and processes with risk-mitigation goals. QUALIFICATIONS - Deep subscription billing expertise: substantial experience with payments, renewals, refunds, disputes/chargebacks/TC40 in a recurring-revenue business (non-negotiable). - Advanced SQL: complex joins, window functions, incremental logic, and performance-aware query writing. - Data visualization: proficiency with Omni, Tableau, Power BI, or similar tools to build dashboards and communicate insights clearly. - People leadership: experience setting priorities, distributing work, reviewing output, and being accountable for a team’s results. - Statistical & analytical rigor: forecasting, experiment design, and translating ambiguous business questions into structured analysis. - Billing platforms / processors: familiarity with tools such as Stripe, Braintree, Nuvei, or similar. - Strong communication: ability to explain complex findings to both technical and non-technical stakeholders. - Cross-functional collaboration: track record of influencing decisions with data across multiple departments. NICE TO HAVE - Python and/or modern analytics tooling (dbt, Airflow, or similar). - Experience building and maintaining ETL/ELT pipelines for billing or financial data. - Risk analysis or fraud-adjacent experience in billing contexts. - Advanced visualization techniques (cohorts, funnels, anomaly detection dashboards). LOCATION Ruby Labs operates within the CET (Central European Time) zone. Applicants from any country are welcome to apply for the position as long as they are located within approximately ± 4 hours of CET. This ensures optimal collaboration and communication during working hours. BENEFITS Discover the perks of being part of our vibrant team! We offer: - Remote Work Environment: Embrace the freedom to work from anywhere, anytime, promoting a healthy work-life balance. - Unlimited PTO: Enjoy unlimited paid time off to recharge and prioritize your well-being, without counting days. - Paid National Holidays: Celebrate and relax on national holidays with paid time off to unwind and recharge. - Company-provided MacBook: Experience seamless productivity with top-notch Apple MacBooks provided to all employees who need them. - Flexible Independent Contractor Agreement: Unlock the benefits of flexibility, autonomy, and entrepreneurial opportunities. Benefit from tax advantages, networking opportunities, reduced employment obligations, and the freedom to work from anywhere. Read more about it here: https://docs.google.com/document/d/1tzxGX4Uu7Ts_HCLFXESKLnKaaBfVCPf1f9AYZPrkjJM/preview?tab=t.0 https://docs.google.com/document/d/1nkrN76JlZkbKj9WSOhlT1_mni_CZeDkHdwfIjPXVwvk/preview?tab=t.0 Be part of our fast-growing team and seize this excellent opportunity for personal and professional growth! INTERVIEW PROCESS After submitting your application, we conduct a thorough review which typically takes 3 to 5 days, but may occasionally take longer due to the volume of applications received. If we see a potential fit, we proceed with the following steps: - Recruiter Screening (40 minutes) - Technical Interview (90 minutes) - Final Interview (60 minutes) LIFE AT RUBY LABS At Ruby Labs, we move fast, aim high, and expect the same from our team. We’re not here to play small—we’re here to build, grow, and win. That means we look for people who are ambitious, driven, and ready to give their best every single day. This is a place for individuals who thrive under pressure, embrace challenges, and see opportunity in every obstacle. If you’re hungry to achieve, motivated by impact, and want to grow at the speed of your own ambition, Ruby Labs offers the platform to make it happen. Here, effort is matched with reward. We recognize those who go all in and deliver results, and we create space for people who want more—more responsibility, more growth, and more success. #LI-Remote

Location: Austin; Yerevan Workplace: OnSite About the role Teza's Data Platform team owns the data the firm trades on: every backtest, every live strategy, every portfolio decision starts with data we ingested, cleaned, stored, and served. The scale, in plain numbers: - 1 PB of raw historical vendor data, growing by ~150 GB every day - 120 TB of processed, query-ready data in historical storage - Thousands of scheduled jobs: run by cron today, actively migrating to Apache Airflow - Alternative data delivered directly into the real-time feeds of live trading strategies This is a hands-on position on a small team of data engineers with growth potential. The firm is looking for outstanding technical skills, strong attention to detail, and a desire to architect and build data platforms. Location Austin, TX / Yerevan, Armenia (in-office requirement) Key Responsibilities - Work directly with Portfolio Managers and Quantitative Developers: turn their requirements into datasets and pipelines, and be the person who knows every nuance of the data they trade on. - Design and onboard new data sources into our warehouse; improve the robustness, speed, and scalability of our systems; manage data entitlements. - Build automated systems for data cleansing, anomaly detection, monitoring, and alerting, bad data must never reach a strategy. - Evaluate new tools and technologies for organizing, querying, and streaming large datasets, and when nothing on the market fits, build it. That's how the bitemporal store happened. - Support the production data warehouse the firm depends on. - Develop and maintain vendor relationships aligned with our business objectives. What we're building right now - A bitemporal data store, designed and written in-house from scratch. Every dataset answers both "what did we know then?" and "what do we know now?", which is what lets researchers trust a backtest. - A Python 3.14 migration of a large, long-lived codebase. - Adoption of the latest Apache Airflow: writing DAGs for the thousands of jobs moving off cron. - Pipelines for market data and alternative data: everything from exchange feeds to weather. - Real-time delivery: alternative data flows straight into strategies' live feeds. Pipelines you build sit in the trading path. - CI/CD for all of it, in GitHub Actions. Our Stack Python and Java · Apache Airflow · Slurm · NATS · PostgreSQL · MongoDB · S3 · NFS · GitHub Actions   Basic Requirements - Proficiency in Python and Unix/Linux for data manipulation, scripting, and automation. - Strong SQL, including query optimization and performance tuning, and familiarity with NoSQL. - A solid grasp of data modeling: normalization and denormalization, and the judgment to know when each applies. Nice to have - Financial industry experience or internships. - Java (part of our platform is written in it). - Experience with on-premises data infrastructure. - Familiarity with a cloud platform (AWS or GCP). - Apache Airflow or similar workflow orchestration tools. Benefits - Health, visual and dental insurance - Flexible sick time policy

Задачи: — Проектировать и разрабатывать ETL/ELT пайплайны по цепочке Source → ODS → DDS → витрины данных. — Строить витрины и схемы под аналитику и отчётность; оптимизировать SQL (индексы, партиционирование, предикативный pushdown). — Настраивать доступ к данным через федеративные SQL-движки, REST/JSON API, батч- и файловые выгрузки, асинхронную обработку тяжёлых выборок. — Вести пайплайны в Airflow с инкрементальной загрузкой, идемпотентностью и контролем качества данных (DQ). — Вести каталог данных, глоссарий и data lineage; следить за согласованностью источников. — Доводить задачи до прода в GitOps-флоу (GitLab CI + ArgoCD), контейнеризация (Docker / K8s). Требования: — Уверенный Python и очень уверенный SQL (оконные функции, оптимизация запросов). — Опыт построения ETL/ELT-пайплайнов и оркестрации (Airflow или аналог). — Понимание моделирования данных и хранилищ (DWH / DataMart, слои ODS/DDS, нормализация, партиционирование). — Опыт с реляционными и аналитическими СУБД (PostgreSQL; плюсом — Vertica / Trino / ClickHouse). — Понимание Git, CI/CD и контейнеризации (Docker), базовое понимание Kubernetes. — Внимание к качеству данных, идемпотентности и инкрементальной загрузке. — Интерес к финансовому домену: транзакции, отчётность, регуляторные данные, справочники. Плюсом будет: — Федеративные движки (Trino / Presto), каталоги данных и data lineage (OpenMetadata / DataHub). GitOps (ArgoCD), стриминг (Kafka), dbt, IaC. — Опыт с финансовыми и регуляторными данными, витринами для отчётности, безопасностью данных. — Базовое понимание LLM/RAG (эмбеддинги, retrieval) — как смежное направление команды. Условия: — Работа с современным data-стеком в финансовом домене национального масштаба. — Менторство и турбо-рост в команде, которая катает продукты в прод каждый месяц. — ДМС для вас и членов семьи. — Оплачиваемый отпуск и 100% официальное оформление.

Location: Singapore, Singapore OKX will be prioritising applicants who have a current right to work in Singapore, and do not require OKX's sponsorship of a visa. Who We Are At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom. OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). OKX is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves. Across our multiple offices globally, we are united by our core principles: We Before Me , Do the Right Thing , and Get Things Done . These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er. OKX is part of OKG, a group that brings the value of Blockchain to users around the world, through our leading products OKX, OKX Wallet, OKLink and more. About The Opportunity We are building the foundational data infrastructure that powers one of the world's leading crypto exchanges. As a Big Data Platform Engineer , you will design, build, and evolve the core platform services that enable hundreds of data engineers and analysts to move fast and ship reliable pipelines. You will be at the forefront of our AI agent initiative — embedding LLM-driven capabilities directly into the platform layer so that scheduling, cost optimization, and incident response increasingly run autonomously. What You’ll Be Doing Platform Core: Design and operate large-scale distributed data systems Own the big data compute and storage infrastructure (MaxCompute/ODPS, Hologres, Spark) Build and maintain multi-site task orchestration that dynamically selects engines and enforces policy Drive reliability and performance improvements across batch and real-time pipelines AI Integration: Build the AI-native platform layer Develop and expose MCP (Model Context Protocol) tool interfaces so AI agents can interact with platform APIs Build the scheduling and cost-optimization agents that auto-tune resource allocation and alert severity Instrument platform telemetry to feed AI-driven SLA monitoring and anomaly detection Design context retrieval pipelines (RAG / vector search) for SQL code and config knowledge bases Tooling & DX: Evolve the developer experience Own the internal data development platform — IDE integrations, code review automation, deployment tooling Build APIs-first tools (backfill, ingestion automation) designed for future MCP integration Collaborate with data warehouse and service teams to define platform contracts Ops & Governance: Drive operational excellence Establish SLA benchmarks, cost metrics, and latency dashboards as AI optimization targets Build automated incident response and root-cause analysis pipelines Define and enforce infrastructure policies across multi-cloud environments AI Agent Ownership — Platform Tier Scheduling Agent: auto-configure task dependencies, engine selection, cost/performance trade-offs, and alert tiers Operations Agent: detect pipeline latency, performance degradation, and schema drift; trigger remediation Incident Response Agent: trace SLA breaches to root cause, assign accountability, generate post-mortems MCP Tool Layer: design and maintain the cross-platform tool interfaces that all agents call into What We Look For In You 5+ years of experience building large-scale data platforms (Hadoop/Spark/Flink or equivalent) Deep expertise in distributed storage and compute systems (MaxCompute, Hologres, ClickHouse, Hive) Strong software engineering skills in Java, Scala, or Python; experience with API-first design Hands-on experience with task scheduling systems (Airflow, DolphinScheduler, or in-house equivalents) Solid understanding of multi-cloud architectures and cost governance Familiarity with LLM integration patterns: tool calling, RAG pipelines, context management Experience with MCP or similar agent-tool frameworks is a strong plus Passion for building systems that make other engineers 10x more productive Perks & Benefits Competitive total compensation package L&D programs and education subsidy for employees' growth and development Various team building programs and company events Wellness and meal allowances Comprehensive healthcare schemes for employees and dependants More that we love to tell you along the process! Notice: All official OKX vacancies are published on this website. While roles may appear on selected third-party platforms from time to time, information on other sites may be inaccurate or outdated. If in doubt, please apply directly through our official careers website. Information collected and processed as part of the recruitment process of any job application you choose to submit is subject to OKX 's Candidate Privacy Notice .

Ищем ML Engineer в команду рекомендаций социальной сети для геймеров Gameram. Вакансия ближе к DE/MLOps — основной фокус на подготовке и доставке рекомендательных систем до пользователя и их стабильной работе в проде. Стек: Scala, Python, Spark, Airflow, Hadoop/HDFS, Kafka, ZooKeeper, ClickHouse, Superset, Redis, PostgreSQL, AWS Обязанности: — Выводить ML-модели и рекомендательные системы в production; — Развивать и поддерживать ML-инфраструктуру; — Взаимодействовать с Data Science, продуктом и другими инженерами. Требования: — 4+ года опыта в разработке; — Уверенный Scala и/или Python; — Опыт вывода ML-моделей в production и поддержки production-систем; — Опыт работы с Big Data и распределённой обработкой данных; — Понимание ML-пайплайнов и жизненного цикла моделей; — Опыт с рекомендательными системами будет большим плюсом. Условия: — full-time, удалённо (не РФ). — Зарплата: $4,000–5,000 gross / месяц.

Задачи: - Разработка и развитие платформенной части продукта: создание библиотек и компонентов, используемых продуктовыми командами, инициирование и продвижение технических улучшений - Участие в проектировании и разработке технического дизайна и архитектуры middleload & peakload приложений - Разработка ETL-процессов с использованием Apache Airflow, претендующих на highload - Активное участие в миграции на микросервисную архитектуру, включая лидирование в end-to-end выносе функциональности - Настройка и улучшение CI/CD-процессов, автоматизация рутинных операций - Внедрение и улучшение инструментов для тестирования и мониторинга - Поддержание и повышение качества кода: написание unit-тестов, проведение Code Review, контроль линтер-политик, усиление типизации Требования: - Опыт коммерческой разработки на Python 3.8 и выше от 5 лет - Опыт работы с async кодом от 3 лет - Опыт коммерческой разработки на Django и FastAPI от 2 лет - Опыт в проработке архитектуры системы, выстраивании межсервисного взаимодействия, умение видеть узкие места - Понимание принципов отказоустойчивости, безопасности и производительности; опыт с highload безопасными системами - Опыт работы с ORM: Django ORM, SQLAlchemy ORM; понимание оптимизации работы с базой, поиск утечек памяти через профилирование кода - Уверенные практические навыки работы с PostgreSQL: декларация сложных запросов, работа с индексами, полнотекстовый поиск, чтение explain - Коммерческий опыт работы с MongoDB: выстраивание структуры хранения данных и оптимизация - Опыт декомпозиции монолитных приложений на микросервисы: от отщепления функционала до запуска микросервиса в продуктив - Коммерческий опыт работы с Kafka от 1 года: написание consumer, producer, handlers, использование consumer group, настройка Kafka - Навыки работы с CI/CD (Gitlab CI) - Умение работать с системами мониторинга и составлять expression для визуализаций по метрикам (Prometheus, Grafana) - Открытость, умение работать в команде и активное предложение путей оптимизации инструментов и процессов

RELOCATION TO LONDON OR BARCELONA | PERK Company Perk is an intelligent platform for travel and spend management, automating manual processes to help businesses reclaim lost productivity. What you will do Design and maintain efficient ELT pipelines using dbt to transform raw data into clean models. Implement best-practice data modeling and software engineering techniques, including CI/CD and testing. Develop comprehensive data quality checks and monitoring scripts to ensure reliability. Collaborate with analysts and business users to optimize data models for specific analytical use cases. Tune SQL queries and data models to improve warehouse performance and reduce latency. Build scalable, compelling data visualizations in Looker for large teams. Requirements 3+ years of experience in a data-focused role such as Analytics Engineer, Data Analyst, or BI Developer. Expert-level proficiency in SQL. Hands-on experience with dbt or similar transformation frameworks. Experience with cloud data warehouses like Snowflake, BigQuery, or Redshift. Solid understanding of dimensional modeling and ETL/ELT principles. Proficiency with Git for version control. Must be based within commuting distance of London or Barcelona. Nice to have Python expertise for automation and orchestration. Experience with semantic layers and orchestration tools like Airflow. Knowledge of modern software engineering practices applied to data. Culture & Benefits IRL-first culture emphasizing connectivity and collaboration. Opportunity to work in a global, diverse environment with teams across 12 offices. Focus on professional growth and impact within a fast-growing SaaS company. Equal opportunity employer committed to diversity and inclusion.

О компании Nexters: международная компания по разработке игр, входящая в холдинг GDEV, с офисами на Кипре, в Армении и Казахстане, а также возможностью удалённой работы. Обязанности: — Проектирование, создание и поддержка масштабируемых конвейеров данных (batch и streaming) — Поддержка и рефакторинг существующих решений — Основная зона ответственности — Bronze и Silver слои платформы данных — Разработка и поддержка моделей data lake / data warehouse — Оптимизация производительности Spark-джобов — Разработка и поддержка dbt моделей и контрактов данных — Внедрение и поддержка мониторинга, наблюдаемости и проверок качества данных — Работа с Kafka-стриминговыми конвейерами — Взаимодействие с командами аналитики, маркетинга, платформы и бэкенда Требования: — Уверенные навыки SQL и Python — Опыт создания ETL/ELT конвейеров в продакшене — Практический опыт работы со Spark/PySpark, особенно в стриминговых сценариях — Понимание распределённых систем обработки и хранения данных — Опыт работы с архитектурами data lake / data warehouse — Практический опыт с Delta таблицами, Parquet и S3 — Опыт работы с оркестраторами, такими как Airflow или аналогичными — Опыт работы с Kafka и стриминговыми конвейерами — Опыт поддержки и улучшения конвейеров данных в продакшене — Опыт с практиками наблюдаемости, мониторинга и качества данных Условия: — Медицинская программа (включая стоматологию) или компенсация, доступная в любом месте работы — Медицинская программа для членов семьи при работе в офисах на Кипре, в Армении и Казахстане — Оплачиваемый отпуск и больничные — Возможность работать в офисах или удалённо — Спортивные активности в офисах (йога, футбол, волейбол) — Пакет поддержки релокации при переезде в официальные локации — Курсы английского языка онлайн, уроки греческого и армянского для сотрудников в соответствующих офисах — Участие в гейм-джемах, хакатонах, конференциях и митапах — Поддержка социальных инициатив и участие в благотворительных проектах Формат работы: в офисах компании на Кипре, в Армении и Казахстане или удалённо.

Location: Hybrid About Us At Cloudflare, we are on a mission to help build a better Internet. Today the company runs one of the world’s largest networks that powers millions of websites and other Internet properties for customers ranging from individual bloggers to SMBs to Fortune 500 companies. Cloudflare protects and accelerates any Internet application online without adding hardware, installing software, or changing a line of code. Internet properties powered by Cloudflare all have web traffic routed through its intelligent global network, which gets smarter with every request. As a result, they see significant improvement in performance and a decrease in spam and other attacks. Cloudflare was named to Entrepreneur Magazine’s Top Company Cultures list and ranked among the World’s Most Innovative Companies by Fast Company. At Cloudflare, we’re not looking for people who wait for a polished roadmap; we’re looking for the builders who see the cracks in the Internet that everyone else has simply learned to live with. We value candidates who have the instinct to spot a "normalized" problem and the AI-native curiosity to create a solution using the latest tools. Our culture is built on iteration, leveraging AI to ship faster today to make it better tomorrow, while ensuring that every improvement, no matter how small, is shared across the team to lift everyone up. If you’re the type of person who values curiosity over bureaucracy, and that AI is a partner in solving tough problems to keep the Internet moving forward, you’ll fit right in. Available Locations: Austin, TX - Hybrid About the team The Data Intelligence & Analytics organization builds the core data platform and internal products that power decision-making across the company. We design and operate large-scale data systems, own the company’s data lake, ingestion infrastructure, and platform tooling, and develop end-to-end applications that transform complex datasets into fast, reliable, business-critical products used daily by go-to-market, product, and engineering teams. Our work sits at the intersection of data platforms, distributed systems, and product development, giving engineers the opportunity to own meaningful problems across the stack and build systems that truly run the business. About the role We are looking for a visionary and hands-on Lead Machine Learning Engineer to join our Austin team. In this role, you will be the principal architect behind the next generation of our unified AI/ML platform, designing and building the infrastructure that powers everything from traditional predictive models to generative AI, large language models (LLMs), and autonomous agent frameworks. You will own the end-to-end technical strategy, blueprint, and execution of scalable backend services and data pipelines that support AI-driven applications across go-to-market, engineering, and product teams. Because our products are initiated and owned entirely within the team, you will drive the vision from initial requirements and system design to global deployment, optimization, and long-term evolutionary ownership. What you'll do Architect and evolve a highly scalable, multi-tenant AI/ML platform that seamlessly unifies traditional ML (classification, regression, forecasting) and Generative AI/LLM orchestration. Design and implement robust production-grade AI Agents and Advanced Chatbots. Build reliable execution environments for Multi-Agent Systems, including state management, long-term memory architectures, and Model Context Protocol (MCP) server integrations. Build high-throughput, low-latency application backends and orchestration layers. Partner closely with data, platform, and full-stack engineers to ensure seamless feature delivery and reliable production operations. Act as a technical anchor for the Data Science team – enforcing rigorous engineering standards, leading design and security reviews, evaluating build-vs-buy decisions, and mapping business requirements to robust technical designs. Evaluate trade-offs and drive adoption of modern AI infrastructure tools, optimized embedding pipelines, vector databases, and serverless compute paradigms (such as Workers AI).\ Must-Have Skills Extensive experience as a Senior or Lead ML Engineer, with a proven track record of architecting and operating production-grade ML platforms, services  and distributed backends. Strong competency in Traditional ML lifecycles (feature stores, training pipelines, model monitoring) alongside deep experience in Generative AI patterns (RAG pipelines, context engineering, fine-tuning, guardrailing, and agentic AI systems). Mastery of Python and robust experience with modern backend ecosystems. Familiarity with (or willingness to collaborate on) full-stack technologies like React and TypeScript is highly valued. A builder's mindset. You are comfortable navigating ambiguity, shaping your own technical roadmap, adapt as needed and taking extreme ownership of system reliability, costs, and model performance. Nice-to-Have Skills Technical Leadership & Systems Architecture 3+ years of dedicated ML Engineering experience within a large-scale, enterprise environment (handling petabyte-scale data and working across globally distributed teams). Proven ability to architect, scale, and secure reliable, highly observable distributed systems, with a track record of leveling up platform foundations. Experience mentoring engineers, leading by example through high-quality code and rigorous design reviews, and fostering a culture of technical excellence. Strong problem-solving skills with a demonstrated ability to independently drive complex projects through ambiguous spaces and collaborate cross-functionally with data engineers, full-stack teams, and analysts. AI, LLMOps & Agentic Engineering Hands-on proficiency in building production-grade GenAI applications and multi-agent systems using advanced LLM frameworks like LangGraph, LangChain, or Autogen. Deep understanding of agent harness primitives, state management, memory architectures, and tool-calling loop mechanics. Experience establishing LLMOps foundations, including automated prompt tracking, LLM evaluation pipelines (e.g., Ragas, TruLens), vector database optimization, context/token management, and real-time guardrailing/moderation layers. Deep experience in scientific computing using Python (Scikit-Learn, PyTorch, or TensorFlow) and deploying traditional systems for end-to-end training, batch/real-time inference, and model observability. Infrastructure, Cloud & Data Platforms Strong experience with Docker and Kubernetes for containerization and orchestration, alongside Infrastructure-as-Code tools like Terraform and public cloud ecosystems (GCP, AWS, or Azure). Hands-on experience with modern MLOps platform tools (e.g., Airflow, Argo Workflows, ArgoCD) and data systems including BigQuery, Postgres, and robust ETL/ELT practices. Experience with full-stack web technologies and serverless/edge environments (FastAPI, TypeScript/JavaScript, Cloudflare Workers), with the agility to contribute across a multi-language stack. Strong foundation in continuous integration/continuous deployment (CI/CD), testing frameworks (Pytest), and robust version control practices. Education & Communication M.S. or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field. Exceptional written and verbal communication skills, with the ability to translate complex technical architectures into clear concepts for both engineering peers and business stakeholders. Equity This role is eligible to participate in Cloudflare's equity plan. Benefits Cloudflare offers a complete package of benefits and programs to support you and your family. Our benefits programs can help you pay health care expenses, support caregiving, build capital for the future and make life a little easier and fun! The below is a description of our benefits for employees in the United States, and benefits may vary for employees based outside the U.S. Health & Welfare Benefits Medical/Rx Insurance Dental Insurance Vision Insurance Flexible Spending Accounts Commuter Spending Accounts Fertility & Family Forming Benefits On-demand mental health support and Employee Assistance Program Global Travel Medical Insurance Financial Benefits Short and Long Term Disability Insurance Life & Accident Insurance 401(k) Retirement Savings Plan Employee Stock Participation Plan Time Off Flexible paid time off covering vacation and sick leave Leave programs, including parental, pregnancy health, medical, and bereavement leave What Makes Cloudflare Special? We’re not just a highly ambitious, large-scale technology company. We’re a highly ambitious, large-scale technology company with a soul. Fundamental to our mission to help build a better Internet is protecting the free and open Internet. Project Galileo : Since 2014, we've equipped more than 2,400 journalism and civil society organizations in 111 countries with powerful tools to defend themselves against attacks that would otherwise censor their work, technology already used by Cloudflare’s enterprise customers--at no cost. Athenian Project : In 2017, we created the Athenian Project to ensure that state and local governments have the highest level of protection and reliability for free, so that their constituents have access to election information and voter registration. Since the project, we've provided services to more than 425 local government election websites in 33 states. 1.1.1.1 : We released 1.1.1.1 to help fix the foundation of the Internet by building a faster, more secure and privacy-centric public DNS resolver. This is available publicly for everyone to use - it is the first consumer-focused service Cloudflare has ever released. Here’s the deal - we don’t store client IP addresses never, ever. We will continue to abide by our privacy commitment and ensure that no user data is sold to advertisers or used to target consumers. Sound like something you’d like to be a part of? We’d love to hear from you! Please note that applicants who progress to the offer stage of the interview process may be asked to attend an in-person interview within one of the Cloudflare Offices or Cloudflare Hubs.  More details about this will be available at that stage of the interview process. This position may require access to information protected under U.S. export control laws, including the U.S. Export Administration Regulations. Please note that any offer of employment may be conditioned on your authorization to receive software or technology controlled under these U.S. export laws without sponsorship for an export license. Cloudflare is proud to be an equal opportunity employer.  We are committed to providing equal employment opportunity for all people and place great value in both diversity and inclusiveness.  All qualified applicants will be considered for employment without regard to their, or any other person's, perceived or actual race, color, religion, sex, gender, gender identity, gender expression, sexual orientation, national origin, ancestry, citizenship, age, physical or mental disability, medical condition, family care status, or any other basis protected by law. We are an AA/Veterans/Disabled Employer. Cloudflare provides reasonable accommodations to qualified individuals with disabilities.  Please tell us if you require a reasonable accommodation to apply for a job. Examples of reasonable accommodations include, but are not limited to, changing the application process, providing documents in an alternate format, using a sign language interpreter, or using specialized equipment.  If you require a reasonable accommodation to apply for a job, please contact us via e-mail at hr@cloudflare.com or via mail at 101 Townsend St. San Francisco, CA 94107.

MDiet — early-stage HealthTech стартап, разрабатывающий AI-powered платформу, которая помогает снижать количество повторных госпитализаций пациентов. Продукт ориентирован на рынок здравоохранения США. Возможна релокация в США при успешном росте компании, обсуждается индивидуально. Tech Stack: • LangGraph + LangChain / Agentic AI • vLLM + Hugging Face • PyTorch + PEFT / LoRA / QLoRA • Vector databases: pgvector, Qdrant, Weaviate, Chroma, Pinecone • Advanced RAG, Prompt Engineering, Embeddings • FastAPI + PostgreSQL • Docker + Kubernetes + async processing: Arq / Celery / Redis Streams • Apache Airflow для data pipelines • AWS Cloud: EC2, S3, SageMaker, Lambda, RDS, Bedrock • Structured output: Outlines / Guidance / Instructor • Evaluation & observability: Langfuse, RAGAS / DeepEval, Prometheus + Grafana • MLOps practices Задачи: • Проектирование и разработка multi-step LLM agents и pipelines для обработки медицинских данных • Разработка production RAG-систем с высоким уровнем grounding и качеством citation • Деплой и оптимизация self-hosted LLM inference через vLLM с фокусом на cost и throughput optimization • Fine-tuning open-source моделей на domain-specific medical data с измеримым улучшением результата • Построение data pipelines и versioning для документов, embeddings и training data • Создание evaluation frameworks, guardrails и систем контроля hallucinations • Реализация reliable structured output и data extraction • Тесное взаимодействие с physicians и Platform Architect Требования: • Сильный hands-on опыт с production LLM systems: RAG, agents, inference • Опыт fine-tuning open-source LLMs: LoRA / QLoRA / PEFT, с измеримыми результатами • Опыт построения data pipelines и versioning для LLM / RAG workloads • Сильные навыки в evaluation, hallucination reduction и structured output • Production Python + FastAPI + async processing • Опыт с self-hosted inference: vLLM или аналогичные решения • Опыт GPU optimization • Английский язык на уровне C1: strong written and spoken communication skills • Bachelor’s degree или выше в Computer Science, Artificial Intelligence, Machine Learning, Data Science или смежной технической области • 5+ лет опыта в ML/AI • 1.5–2+ года опыта в production LLM projects • Опыт с MLOps Будет плюсом: • Опыт в medical / clinical NLP или regulated domains • Cost modeling и optimization of LLM workloads • MLOps и production fine-tuning pipelines • Apache Airflow, Prometheus / Grafana или AI Solution Architecture • Publications in relevant fields Условия: • Работа в early-stage HealthTech startup с продуктом для US healthcare market • Возможность участвовать в создании AI-powered платформы с реальным влиянием на healthcare-процессы • Офисный формат в Астане первые 7–9 месяцев, далее возможен hybrid/remote • Зарплата $4 000 – $5 000 USD gross + equity • Возможность релокации в США при успешном росте компании, обсуждается индивидуально

Задачи: — Разрабатывать и внедрять классические ML-модели для оценки рисков, выявления мошенничества, прогнозирования оттока и ценообразования. — Создавать функции на базе больших языковых моделей (LLM) и внутреннюю автоматизацию с использованием RAG-пайплайнов и агентных рабочих процессов. — Определять стратегию в области искусственного интеллекта, включая стандартизацию инструментов и разработку фреймворков оценки моделей. — Отвечать за полный жизненный цикл ML: от определения требований заинтересованных сторон до развертывания и мониторинга. — Сотрудничать с руководителем отдела данных для формирования возможностей ML/AI с нуля. Требования: — Опыт профессиональной работы ML-инженером не менее 4 лет с использованием Python в продакшене. — Глубокие знания классического машинного обучения, включая инженеринг признаков и валидацию моделей. — Практический опыт работы с фреймворками LLM, такими как LangChain, LangGraph или LlamaIndex. — Подтвержденный опыт создания систем RAG или продуктов на базе LLM для реальных пользователей. — Опыт работы с основными облачными провайдерами (AWS, GCP, Azure) и контейнерами. — Свободное владение английским языком в письменной и устной форме. Желательно: — Опыт работы с MLOps-инструментами, такими как MLflow, Airflow или Kubernetes. — Опыт работы в сферах финтеха, платежей или телекоммуникаций. Условия: — Комплексный страховой пакет, включающий медицинское, стоматологическое и профессиональное страхование. — 30 дней ежегодного отпуска и дополнительная оплата по уходу за ребенком. — Пособие на оздоровление в размере 5000 SEK и скидки на абонементы в спортзал. — Пенсионный план и программа бонусов на основе результатов работы. — Международная и разнообразная команда в Стокгольме.

Location: Tbilisi; Yerevan; Belgrade; Novi Sad, Vojvodina; Poland; Portugal Workplace: remote About Provectus Provectus is an AWS Premier Partner and an Anthropic Strategic Partner, working at the frontier of applied AI. We help enterprises turn Claude, agentic systems, and their own data into measurable business outcomes — through bespoke applications, managed services, and advisory engagements. With offices in North America, LATAM, and EMEA, we partner with clients worldwide. Our work centers on two verticals — Financial Services & Insurance and Healthcare & Life Sciences — where we deploy five pre-built AI Blueprints: Submission Flow, Portfolio Lens, Asset Flow, Revenue Flow, and Evidence Lens. Each Blueprint rebuilds a critical business process front to back, shipped from working code and tuned to a client's specific book, regulators, and operating posture. Our team holds 100+ AWS certifications, is Claude Code certified, and co-delivers Anthropic's Agentic SDLC program, Cowork Activation, and AI Blueprint engagements. Where this role sits: You will work in a small, senior pod alongside an FDX; our delivery arc is Sprint → Enable → Realize: Forward Deployed Executive (FDX) owns the commercial relationship and the business outcome. Works alongside the client's leadership or C-suite level to move the client's KPIs — revenue growth, cost reduction, risk reduction. Forward Deployed Engineer (FDE) embeds with a client to change how that client operates. You own the method; nobody hands you a ticket. You map the client workflow as it actually happens, identify the business problem underneath it, build a working AI solution, present to the client in the language of outcomes, and transfer it. Provectus maintains industry Blueprints — working systems that have already shipped for a client in the same industry — so you begin from running code and tuning it to this client's specific book, regulators, and operating posture. You will be measured on whether the Business Unit's number moved, not on hours or scope delivered. Requirements: Mindset Proactive and self-directed; identify problems before they're handed to you Comfort with ambiguity and ownership. Engagements start underspecified by design. Closing that gap is the job B2+ English, comfortable collaborating across distributed, multicultural teams Client Engagement You are willing to spend time understanding and doing someone else’s job on the client's side before you write a line of code Credible with senior stakeholders — you can hold a redesign conversation with a BU head and a scoping conversation with a CTO, presenting outcomes to them You can produce a scoped, phased delivery plan with clear deliverables, dependencies, and risks — and estimate what it will cost to build and to run Technical depth 7+ years building and running production systems. Solid AI/ML foundations. You understand what the models do well enough to reason about failure modes Designed and shipped to production LLM applications and agentic workflows — not demos, not POCs, not notebooks Agentic orchestration: multi-step workflows, graph-based orchestration, tool use, state management, and recovery from partial failure Experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) and agent frameworks. Experience building and optimizing RAG systems in production Strong engineering fundamentals — dropped into an unfamiliar codebase or language, you’re productive. Python and/or TypeScript proficiency; depth matters more than stack. Experience in making and defending architectural trade-off decisions Hands-on AWS production depth: Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, ECR, or similar. GCP or Azure is a plus Cloud-native delivery: containers, ECS or Kubernetes, IaC, and CI/CD applied to AI pipelines You evaluate. You have built or owned an eval suite for a non-deterministic system, and you can explain what you measured, how you produced ground truth, and what gated a release Model and agent monitoring, drift detection Cost and latency discipline: model tiering, caching, and the ability to say what a workload costs to run before it runs Hands-on production experience with the Claude ecosystem —  Claude Code, CLAUDE.md, hooks, skills files.  Spec-driven development — writing the intent, constraints, and acceptance criteria before you let an agent build — is a strong plus MCP: you can say why an agent would prefer it to a REST integration. Having authored a server is a plus What you’ll do: Take the seat Sit with the client and the Forward Deployed Executive at the start of an engagement. Learn the function from inside, not from a requirements doc, and redesign the function from first principles. Reach working fluency in a new domain — insurance underwriting, healthcare revenue cycle, asset flow. Build Design and ship production GenAI systems into the customer’s environment (cloud-native data, LLM-based, and agentic AI solutions). Implement and optimize RAG systems for production use cases Build the evaluation harness before you build the feature. Define what working means, instrument it, and let the evals drive the design. Write production code across the stack — AI, backend services, data pipelines. We choose tools to fit the customer. Take systems to production on AWS (GCP or Azure where the customer requires it): containerised, CI/CD, automated testing, monitoring, and maintainable after we leave. Hand the system over to the client. Start from the blueprint, and feed the blueprint. What you learn in the field becomes the baseline the next engagement starts from. Lead architecture reviews, produce technical design documents, and contribute to standards. Mentor engineers and share knowledge across the team. Own the outcome. Work in a pair with a FDX who carries the Business Unit’s KPIs. Your work is measured against the same number. Own the technical direction of technical proposals and scoping. Drive adoption. Change management is part of the engineering job here. Be credible with the customer’s engineers and their executives. Shape what we commit to before we commit to it. Nice to Have: Prior experience as a founder, CTO, or engineering leader who has chosen to return to individual contribution Experience in one of the industries: financial services, insurance, healthcare Consulting, professional services, or other embedded customer-facing delivery A2A: you can explain agent-to-agent interoperability AWS and Claude Code Certifications CI/CD pipeline experience (GitHub Actions, GitLab CI) Experience in an additional language (Go, TypeScript, or Rust) Experience with Apache Spark, Apache Airflow, Kafkа What We Offer: The chance to shape how leading enterprises across LATAM, Europe, and North America adopt AI, from strategy through first deployment A forward-deployed model working in small, senior teams alongside Principal Architects and Forward Deployed Engineers A growing AI delivery practice where you help build the tooling and frameworks, not just use them Remote-friendly culture Internal training programs with full support for Claude, AWS, and other professional certifications, conference attendance Career growth; we actively develop our engineers Access to the latest AI tools and premium subscriptions Long-term B2B collaboration Private medical insurance or a budget for your medical needs Paid sick leave, vacation, and public holidays Equipment and all the tech you need for comfortable, productive work How we hire:   Intro conversation. The role, your background and aspirations, tech questions. Technical interview with live engineering sessions. Real problems, your own editor, you may use an LLM assistant  HR Interview. Soft skills and expectations HM interview. Tech questions; a live engineering session is also possible

Location: United States What The Role Is As a Staff Engineer at Babylist, you own some of our hardest domains and decide where they go. Millions of families depend on what we build. Agents write most of the code now. So the hard part is yours: what to build, how it should work and whether what shipped was right. You're still in the code for the genuinely hard problems. Agents handle the volume. You spend your time on the parts that need a person. We're building well beyond the registry now: the financial side of raising a kid, maternal health made simpler and more human, the education new parents are looking for and the community around them. The hard problems run across all of it, plus the platform underneath and the AI we already ship to families. You'll own a big piece of the product, but you won't be boxed into it. The roadmap is open. You have a say in which bets we make, and you pick what you take on next. What You'll Own A Staff Engineer here sets direction. You own a domain: a product surface, a platform area or a capability that cuts across several teams. You set where it's going over the next year or two, sequence the bets that get there and make the technical and product calls along the way. You're still in the code. We don't have architects who've stopped building. If you stepped away, multiple teams would feel it. Your influence shows up in the systems and the work other teams choose to build on. You don't need direct reports to have that reach. It comes from what you build. In practice, you: Take a fuzzy business problem from the first sketch through to production, and stay on the hook for whether it actually helped customers. Build the platform or tooling other teams adopt by choice, and own it as it scales. Make the architecture calls that span teams and the ones that are expensive to reverse. Set the standard for how your domain builds with AI. Decide what good looks like, build the patterns and evals that get agents there and catch the output that's confidently wrong before it ships. Partner with product, design and data as a peer, shaping what's worth building from the start. Coach Senior engineers through the hard calls, the ambiguous ones as much as the technical ones. A few problems people at this level are working on right now: Resolving one customer across registry, shop and health, plus the friends and family buying for them, so personalization works everywhere without each team rebuilding it. Designing the knowledge system our coding agents reliably load, and working out how much context actually helps before it starts to hurt. Building Early Investor, our new family finance product, from scratch, on a deadline that can't move. Who You Are You've shipped production systems for enough years to have earned strong opinions, and you hold them loosely. You can pick up an ambiguous problem and start moving before anyone hands you the full picture. You've already changed how a team builds with AI, and the new way stuck. Most Staff engineers lean one of two ways, and both do well here. Some point their depth at the systems everyone runs on: the platforms, the reliability and security bar, the harnesses that make AI produce good code. Others point it at the customer: framing the problem with PMs as peers, owning a journey end to end, deciding what to build and learning whether it worked. You don't have to be both. You do have to be excellent at one and fluent in the other. A few things that tend to be true of people who thrive here: You measure yourself by impact: a customer outcome, or a system a dozen teams come to depend on. You're curious: you spot problems before they're filed and push your own ideas until they ship. Compensation We post real numbers. For a US-based Staff Engineer, the starting base salary range is $233,500 to $290,700, plus a target annual bonus of 20 percent of base. That's total target cash of roughly $280,200 to $348,840. On top of that you get meaningful equity and a 401(k) match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope. How We Build AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome. The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers. The Stack Core app: Rails, Packwerk, React, TypeScript, Sidekiq Mobile: iOS (Swift), Android (Kotlin) Data, search & events: MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma Machine learning: deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow AI & dev tooling: Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim Infra & ops: AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly Key integrations: Shopify (payments), Iterable (CRM) Why Babylist An engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done. Ten million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide. How We Work Remote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis. How We Hire Three rounds, usually two to three weeks start to finish. Recruiter conversation (30 minutes). Trade context: what you want next, what we're building and straight answers on comp, team and remote. Technical screen (1 hour). One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles. Final round (4 hours). Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours. If your timeline is tight, tell us and we'll move faster. Benefits Company-paid medical and fully covered dental and vision A 401(k) match Generous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program Winter Wonder Week, a paid company-wide week off at the end of the year A remote-work stipend Mental-health and wellness support A Few Things To Know We record and transcribe interviews to evaluate candidates, in line with applicable privacy laws. We expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking. If you have a family member or close relationship with a Babylist employee, let your recruiter know. Official outreach only ever comes from an @babylist.com address

Задачи: — Распил backend-части на микросервисную архитектуру — Разработка новых бизнес-фич: алерты, отчеты, витрины, интеграции с Jira и BI-системами — Масштабирование пайплайнов в Apache Airflow — Работа в Kubernetes, использование Docker и Helm для деплоя — Автоматизация CI/CD, усиление покрытия тестами и улучшение наблюдаемости (Prometheus + Grafana) Требования: — Уверенное знание Python 3.x, опыт коммерческой разработки от 3 лет — Практический опыт работы с FastAPI (разработка REST API, работа с зависимостями, валидацией, middlewares) — Опыт работы с асинхронным стеком (async/await, aiohttp, asyncpg) — Навыки написания тестов (pytest) и отладки приложений — Умение писать чистый, читаемый и тестируемый код (принципы SOLID, PEP8, code review) — Уверенное владение SQL, опыт работы с PostgreSQL (написание запросов, индексация, оптимизация) — Знание принципов контейнеризации, опыт работы с Docker — Понимание основ Kubernetes: деплой приложений, работа с pod-ами, secrets, configmaps

Задачи: - Управление AI-продуктами от идеи до вывода в прод. - Участие в проработке AI-стратегии кластера (Roadmap, KPI, приоритизация инициатив). - Проработка архитектуры AI-решений. - Координация команд: Data Scientists, ML-инженеры, Data Engineers. - Решение проблем масштабирования моделей и качества данных. - Запуск PoC и MVP AI-продуктов. - Оптимизация инференса моделей (скорость, стоимость, точность). - Дообучение open-source моделей под задачи бизнеса. Требования: - Образование: высшее. - Опыт работы: от 5 лет на релевантной позиции. - Опыт в предметной области AI. - Опыт коммерческой разработки, развитый технический кругозор. - Навыки презентации для C-level с ценностными тезисами. - Agile/Scrum для ML-проектов. - Языки: Python, SQL. - ML/DS: scikit-learn, TensorFlow/PyTorch, Hugging Face, LangChain. - MLOps: MLflow, Kubeflow, Airflow, Docker/Kubernetes. - Данные: Spark, DWH (BigQuery, Snowflake), векторные БД (FAISS, Pinecone). - Опыт управления коллективом: обязателен. Требования к личным качествам: - Лидерство, выстраивание коммуникаций, синхронизация работ, критическое мышление, предпринимательский mindset, адаптивность. Знание стека: - LangChain, Kafka, K8s, LLM-модели, NLP-модели — embedding, NER, classification.

Задачи: — построение масштабируемых конвейеров данных и инфраструктуры в облачной среде GCP. Требования: — опыт работы Data Engineer от 3 лет; — уверенное владение GCP (BigQuery, Cloud Storage, Dataform); — продвинутые навыки Python и Apache Airflow; — уровень английского B2 и выше. Условия: — долгосрочный контракт с QIC; — полный рабочий день, удалённо, график 5 дней в неделю, с воскресенья по четверг; — отпуск по календарю праздников Катара, 20 рабочих дней отпуска и 10 дней больничных; — оценка эффективности два раза в год; — испытательный срок 3 месяца; — поддержка цифровых кочевников. Преимущества и бонусы: — индивидуальные языковые занятия; — страхование RemoteHealth Premium с покрытием до $5000 в год; — образовательный бюджет; — консультации по налогам с менеджером; — клуб бега QIC в Strava; — корпоративные скидки в программе Privilege, включая Qatar Airways.