Вакансии

Location: Москва Workplace: Гибридный О компании Big Data — один из ключевых юнитов МТС. Сегодня Big Data — это 20 петабайт данных, 5 000 метрик на профиль и 400 увлечённых профессионалов в команде. Наши технологии применяются практически во всех направлениях компании — маркетинг, обслуживание, планирование сети, HR, розница. Один из ключевых продуктов — рекомендательная платформа, которая использует всю мощь ML и больших данных для подбора наиболее релевантных айтемов для клиентов из каталогов МТС. Мы уже поставляем рекомендации в онлайн-кинотеатр КИОН, в читалку «Строки», МТС Банк, сервисы по продаже билетов, интернет-магазин и другие продукты экосистемы МТС. Сейчас мы объединяем рекомендательные движки в единую платформу, создаём универсальный поисковый движок, realtime-триггеры и масштабируем решения на все сервисы компании. Сейчас находимся в поиске Middle ML-инженера в RecSys. Стек - Python, Hadoop, Pytorch, Catboost, Triton, vLLM, FastAPI, k8s, PostgreSQL, Aerospike, Kafka, Airflow, GitLab CI, Grafana Обязанности Разрабатывать и обучать рекомендательные ML-модели от идеи до продуктивизации; Формулировать и проверять гипотезы по улучшению рекомендаций для роста бизнес-метрик клиентов; Внедрять ML-пайплайны в real-time сервисы и доводить модели до продакшена; Работать с рекомендательной платформой: использовать готовые наработки и активно контрибьютить в её развитие; Проводить исследовательские задачи — читать статьи, воплощать новые подходы, в том числе генеративные полки на основе предпочтений пользователей; Взаимодействовать с дата-инженерами по доработке витрин и пайплайнов, с аналитиками — по постановке и проверке A/B-тестов. Требования Уверенные навыки Python и хорошее знание теории вероятностей и математической статистики; Коммерческий опыт в Data Science от 2 лет и понимание ML-архитектуры — не только эксперименты в ноутбуке, но и реальные внедрения в продукт; Опыт разработки и продуктивизации ML-сервисов (от моделей до API); Опыт внедрения ML-пайплайнов в real-time сервисы; Опыт работы с шедулерами (Airflow или аналог), pyspark и SQL/NoSQL базами данных; Самостоятельность: умение предлагать технические и архитектурные решения, технологии и направления улучшений; Хорошие коммуникативные и аналитические навыки. Условия Собственную платформу MTS Ocean для получения ИТ-ресурсов, а это значит, что деплой, мониторинг, observability — не будут для тебя проблемой, ты сможешь сосредоточиться на фичах; Профессиональные гильдии инженеров, где мы поддерживаем друг друга и помогаем стать лучше; Внутреннюю площадку TechTalks для обмена опытом, дискуссий, развития навыков самопрезентации; Участие во внешних ИТ конференциях. Мы выступаем на HighLoad++, DataFest, Mobius, Test Driven Conf, Joker, DevOps, Матемаркетинг и даже проводим собственную конференцию по архитектуре True Tech Arch; Полезные курсы и вебинары в корпоративном университете и электронную библиотеку; ДМС с первого месяца работы, включая стоматологию; Страхование от несчастных случаев с 1 месяца работы. Материальную помощь в сложных жизненных ситуациях; Отпуск 28 календарных дней (+3 дополнительных дня за ненормированный рабочий день); Гибридный формат работы; Прием врачей общей практики и массаж в офисе; Мобильная связь за счет компании и льготные тарифы для близких; Подписка на онлайн-кинотеатр KION, сервис МТС Музыка, книжный сервис Строки от МТС, безлимитные мессенджеры и соцсети.

Location: Москва Workplace: Гибридный О компании Мы развиваем платформу мониторинга для ПАО МТС и в поисках специалиста, который сможет разработать ML/AI-агента для исследования инцидентов. Обязанности ML/AI‑исследование инцидентов – разработка и внедрение моделей машинного обучения для автоматической классификации, корневого анализа и предсказания серьёзности инцидентов. Контекстный агент – создание сервис‑агента, который в режиме реального времени собирает и коррелирует диагностические данные (логи, метрики, события) из NMS/EMS, систем мониторинга и баз знаний. Логика принятия решений – построение правил и алгоритмов, определяющих, когда система может автоматически выполнить действие, когда требуется подтверждение оператора и когда действие запрещено. Auto remediation – разработка и интеграция runbook/playbook‑сценариев, позволяющих автоматически восстанавливать сервисы; автоматический выбор оптимального сценария и контроль его исполнения. Интеграция со стеком эксплуатации – подключение AI‑агента к платформам мониторинга, incident‑management (Jira/ServiceNow), NMS/EMS и системам диагностики через API/событийные шины. Объяснимость и guardrails – построение механизма, который генерирует понятные обоснования предложенных причин/действий и накладывает безопасные ограничения (guardrails) для исполнения в production‑среде. Оценка качества и совместная работа с эксплуатацией – мониторинг метрик (precision, recall, FP/FN, доля автоматизированных решений, MTTR), работа с историей инцидентов и совместная формализация типовых сценариев диагностики/восстановления с командой эксплуатации. Требования Практический опыт в ML/AI – работа с applied‑machine‑learning, LLM‑агентами, NLP и решениями интеллектуальной автоматизации. Опыт обработки неструктурированных и полуструктурированных данных – аварийные сообщения, логи, описания инцидентов, комментарии операторов. Построение и эксплуатация пайплайнов – создание ETL‑/ELT‑конвейеров, интеграция обученных моделей в production‑окружение. Знание современных подходов к Retrieval‑Augmented Generation и агентным workflow – tool‑calling, классификация, ранжирование, поиск похожих кейсов. Разработка сервисов на Python – чистый, тестируемый код, применение фреймворков (FastAPI, Flask, Django и др.). Работа с API, базами данных и системами обмена сообщениями – REST/GraphQL, SQL/NoSQL, Kafka/RabbitMQ, etc. Понимание безопасной автоматизации и её внедрения в реальный процесс – механизмы approvals, rollback, audit‑trail, RBAC и умение «перевести» модель из эксперимента в продуктивный сервис. БУДЕТ ПЛЮСОМ: Опыт в телекоме, NOC, мониторинге, incident management или эксплуатации инфраструктуры. Опыт работы с LLM-агентами для диагностики, troubleshooting или автоматизации операций. Опыт построения recommendation systems или case-based reasoning. Опыт работы с Kubernetes, Kafka, Airflow, MLflow, векторными БД. Понимание сетевых технологий, базовых принципов работы мобильной сети, EMS/NMS-систем. Опыт создания систем auto-remediation в SRE/DevOps/Network Operations. Условия Оклад + годовая премия; Программа ДМС с первого месяца работы - это 100% покрытие расходов в частных клиниках, включая стоматологию. Застраховать близких родственников можно со скидкой 50%. Для любителей путешествий - страховка в поездках за границу. А еще есть страхование жизни; Очное и онлайн-обучение в Корпоративном университете, у внешних провайдеров, если это полезно для работы, обмен опытом во внутренних профессиональных сообществах, а также доступ к онлайн-библиотеке с полезными книгами и периодикой; Оплачиваемые выходные на значимые события в жизни сотрудника; Материальная помощь в сложных жизненных ситуациях; Корпоративная мобильная связь + интернет, а также единая подписка МТС Premium (онлайн кинотеатр KION, сервис МТС Music, МТС Строки и т.д.); Предложения от партнёров для путешествий, развлечений и занятий спортом; Возможность участия в волонтерских программах: мы организуем благотворительные ярмарки, Образовательные проекты для детей, конкурсы социальных проектов и экологические проекты; Внутренняя спортивная программа «В движении»: забеги, марафоны, турниры по разным видам спорта, медитации и занятия на баланс. Голосуй за МТС в Рейтинге работодателей hh.ru : https://rating.hh.ru/poll Ищи нас в блоке «Технологии и цифровые решения» — направление «Телекоммуникации, связь».

- Продуктовая аналитика и мониторинг выдач, СП, NPL (автозалоги, автокредит, недвижимость, беззалоговые), оценка эффекта продуктовых изменений.

Поддерживать Linux-серверы, Docker-контейнеры и приложения в Kubernetes: проверять состояние, ресурсы, сетевую доступность, логи и причины перезапусков. Следить за доступностью сервисов и ключевыми показателями инфраструктуры, настраивать полезные оповещения, разбирать инциденты до причины.

Location: San Francisco, CA • New York, NY • United States Figma is growing our team of passionate creatives and builders on a mission to make design accessible to all. Figma’s platform helps teams bring ideas to life—whether you're brainstorming, creating a prototype, translating designs into code, or iterating with AI. From idea to product, Figma empowers teams to streamline workflows, move faster, and work together in real time from anywhere in the world. If you're excited to shape the future of design and collaboration, join us! Our Integration Engineering team connects the systems and data that power Figma’s business operations. We partner with teams across Finance, GTM, People, and Engineering to build reliable integrations and data pipelines across Salesforce, NetSuite, Workday, Snowflake, and other core applications. We’re looking for a Data & Integration Engineering who combines application integration expertise with hands-on data engineering experience. You’ll build workflows that move business processes forward and pipelines that make data accurate, accessible, and useful for operations, reconciliation, and reporting. This is a hands-on individual contributor role with end-to-end project ownership, from requirements and architecture through development, deployment, and production support. You’ll help shape our technical direction, mentor engineers, and establish reusable patterns that improve how we build and operate integrations and data pipelines. This is a full time role that can be held from one of our US hubs or remotely in the United States. What you’ll do at Figma: Design, build, and operate integrations across CRM, ERP, and HR systems using MuleSoft, APIs, and event-driven architectures Build and maintain batch and incremental data pipelines into Snowflake, using SQL and Python for transformations, validation, and automation Develop data models and datasets that support business operations, reconciliation, and reporting, partnering with Data Engineering on standards and ownership Work with business stakeholders and product managers to translate requirements into technical designs, delivery plans, and measurable acceptance criteria Support business-critical order-to-revenue workflows with reliable processing, clear ownership, and safe recovery from failures Implement automated testing, monitoring, alerting, and data quality checks to improve reliability and reduce manual intervention Apply security and SOX/ITGC-aligned development practices, including access controls, code reviews, CI/CD, and change management Lead design reviews, mentor engineers, and establish reusable components, documentation, and engineering practices We’d love to hear from you if you have: 5+ years of relevant engineering experience, including hands-on delivery of both enterprise integrations and production data pipelines Strong experience building and supporting API-led integrations, with an understanding of API design, authentication, asynchronous processing, retries, and idempotency Hands-on proficiency in query languages and programming for data transformation, pipeline development, and automation Experience with Snowflake or a comparable cloud data warehouse, including data modeling, incremental processing, and query optimization and integrating enterprise business applications across Finance, GTM, or People systems A track record of independently owning projects from requirements through production, including testing, monitoring, troubleshooting, and documentation, while collaborating with business stakeholders and engineering teams While it’s not required, it’s an added plus if you also have: Experience with dbt, Airflow, or comparable transformation and orchestration tools, as well as change data capture, streaming pipelines, schema evolution, and historical backfills Familiarity with quote-to-cash, billing, accounting, or financial reconciliation processes Experience with Workday Studio, Salesforce Apex, or NetSuite SuiteScript Knowledge of AWS services such as S3, Lambda, and SQS A background in implementing or maintaining SOX/ITGC controls, as well as mentoring engineers and establishing shared engineering standards At Figma, one of our values is Grow as you go. We believe in hiring smart, curious people who are excited to learn and develop their skills. If you’re excited about this role but your past experience doesn’t align perfectly with the points outlined in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. Pay Transparency Disclosure Job level and actual compensation will be decided based on factors including, but not limited to, individual qualifications objectively assessed during the interview process (including skills and prior relevant experience, potential impact, and scope of role), market demands, and specific work location. Figma offers equity to employees, as well as a competitive package of additional benefits, including health, dental, and vision coverage; retirement benefits with company contributions; parental leave and reproductive or family planning support; mental health and wellness benefits; and paid time off. Figma provides paid sick leave, holidays, and other leave benefits in compliance with applicable federal, state, and local laws, including the requirements of the Washington Minimum Wage Act and related regulations. Exempt employees are eligible for employer‑provided paid flexible PTO in addition to flexible paid sick leave. PTO is subject to manager approval. Additional benefits may include company recharge days, cell phone and home internet reimbursements, and a number of lifestyle spending accounts. Figma also offers sales incentive compensation for most sales roles and an annual bonus plan for eligible non-sales roles. All compensation and benefits are subject to applicable plan terms and may be modified by Figma at any time, consistent with applicable law. Annual Base Salary Range: $153,000 — $296,000 USD At Figma we celebrate and support our differences. We know employing a team rich in diverse thoughts, experiences, and opinions allows our employees, our product and our community to flourish. Figma is an equal opportunity workplace - we are dedicated to equal employment opportunities regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity/expression, veteran status , or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. We will work to ensure individuals with disabilities are provided reasonable accommodation to apply for a role, participate in the interview process, perform essential job functions, and receive other benefits and privileges of employment. If you require accommodation, please reach out to accommodations-ext@figma.com . These modifications enable an individual with a disability to have an equal opportunity not only to get a job, but successfully perform their job tasks to the same extent as people without disabilities. Examples of accommodations include but are not limited to: Holding interviews in an accessible location Enabling closed captioning on video conferencing Ensuring all written communication be compatible with screen readers Changing the mode or format of interviews To ensure the integrity of our hiring process and facilitate a more personal connection, we require all candidates keep their cameras on during video interviews. Additionally, if hired you will be required to attend in person onboarding. By applying for this job, the candidate acknowledges and agrees that any personal data contained in their application or supporting materials will be processed in accordance with Figma's Candidate Privacy Notice . Application limits: To help candidates focus on the opportunities most relevant to their skills and experience, Figma limits the number and frequency of applications. These limits apply to all roles: Candidates may submit up to 5 applications during any rolling 30-day period. Candidates may not reapply to the same role while an application is active or within 90 days of receiving a rejection. Once the applicable days has passed, candidates are welcome to apply again.

Задачи: - Разработка моделей данных и отчетных решений в Power BI и Apache Superset с учетом бизнес-задач; - Создание и оптимизация сложных SQL-запросов, реализация dbt-моделей для трансформации данных;

6+ лет в data engineering, опыт с банковскими данными обязательно. Сильный бэкграунд до AI: руками строил DWH или Lakehouse и знаешь, где всё ломается.

Location: Poland Workplace: remote About airSlate airSlate is a global SaaS technology company that develops no-code workflow automation, electronic signature, and document management solutions. Our award-winning products - SignNow, pdfFiller, DocHub, altaFlow, Instapage, and US Legal Forms - serve over hundreds of millions of users and more than one million customers worldwide, helping organizations of every size digitize processes, improve efficiency, and transform how they work.   We’re in an exciting phase of growth and transformation, with teammates in more than 20 countries across three continents and main hubs in the United States, Poland, Ukraine and Philippines.  At airSlate, we’re building value for customers and a culture where growth and innovation go hand in hand. We’re looking for people eager to shape products, scale a company, and thrive in a fast-moving environment. We are looking for a hands-on leader to run Data Enablement. The team that owns how our data is modeled, trusted, and made ready for the business and for AI. You will lead analytics engineers and web analytics specialists, own the semantic layer end-to-end, and turn raw product and event data into curated, well-documented datasets that people can rely on. WHAT YOU WILL DO: Lead and grow the team. Set priorities, coach, and keep a steady delivery rhythm across analytics engineering and web analytics. Own the semantic layer. Design, build and evolve SQL-based models and metric definitions so that one number means one thing across all our products. Raise data curation and automate the routine. Move the team from ad hoc requests to reusable, tested, documented datasets that people and our AI data assistant can query directly, with tracking checks and data-quality monitoring on autopilot. Set standards and partner across the data organization. Modeling conventions, version control, testing and release practices, code review; work with Data Business Partners, Data Science / ML and Data Platform on scope and ownership. Stay close to the code on critical builds. WHAT WE ARE LOOKING FOR: Experience. 5+ years in analytics, data or BI engineering, including 2+ years leading a team or leading others technically. Expert SQL and Python. You set query and performance standards others follow, rewrite expensive queries and explain the trade-offs, handle incremental loads, deduplication and slowly changing dimensions, and own production Python components that other teams depend on. Data modeling and pipelines. You design warehouse and mart models that several teams build on, including a semantic layer, and choose the methodology (star schema, Data Vault or equivalent) to fit the queries it must serve. You optimize and troubleshoot pipelines on a cloud stack such as AWS (Athena, Redshift, Glue) and orchestration tools such as Airflow. Engineering discipline. You set branching and code review standards, require schema and data tests and CI before release, and lead risky changes with a clear rollout and rollback plan. Data quality and data fluency. You design validation that catches problems before stakeholders do, fix root causes rather than symptoms, set documentation standards, and investigate unfamiliar sources before trusting them. Business impact. You design solution options for complex problems, split cross-team work into pieces with a clear definition of done, build dashboards around the decision they support, and become a trusted partner to business leaders. AI in everyday work. You redesign team workflows around AI tools to drive adoption, and evaluate AI outputs for quality, privacy and bias. Leadership and ownership. You own complex team-level projects, make high-impact decisions from complex information, coach people by spotting skill gaps and giving regular feedback, set clear scope expectations, and turn change into actionable steps for the team. Communication. You clarify complex ideas, build consensus across teams and time zones, and communicate in fluent English. Nice to have: experience with dbt or a similar transformation framework (we may adopt one); web analytics and tracking (GTM, GA4); B2B SaaS or document-workflow / e-signature background. What we offer   Flexible working environment - Our teams operate across the globe. We value in‑person collaboration in our hubs, but we also embrace remote and hybrid working. You can work from one of our offices in the United States, Poland, Romania or Ukraine, or remotely from many countries.   Competitive compensation and stock options - We offer salaries that reflect local market conditions and experience,  plus a performance-based bonus system and stock options so you share in the company’s growth.   Professional growth and learning - We invest in your development through courses, conferences, and access to learning resources. We encourage career growth and internal mobility, supporting teammates who want to explore new opportunities within airSlate.   Health and well‑being - We provide comprehensive benefits tailored to each country, including health coverage, wellness programmes and access to fitness options. We also dedicate quarterly company-wide Mental Health Days, when everyone takes time off to rest and recharge.   Family‑friendly culture - Family life is part of who we are, and we embrace it in many forms. From flexibility for parents to our airSlate Junior Club for kids, to company-wide family days and our pet-friendly approach, we’re committed to making work and life easier to balance.   Giving back - We support charitable initiatives around the world through the airSlate Care programme. Our current focus includes humanitarian aid in Ukraine and other regions, matching employee donations and supporting causes chosen by our teams.   Open communication - We encourage transparent dialogue at all levels. From team discussions to company-wide Q&A sessions with our CEO, we make sure everyone has the chance to be heard and to influence how we grow.       It is airSlate's policy to comply with all applicable equal employment opportunity laws by making all employment decisions without unlawful regard or consideration of any individual’s race, religion, ethnicity, color, sex, sexual orientation, gender identity or expressions, transgender status, sexual and other reproductive health decisions, marital status, age, national origin, genetic information, ancestry, citizenship, physical or mental disability, veteran or family status or any other basis protected by applicable national, federal, state, provincial or local law. airSlate's policy prohibits unlawful discrimination based on any of these impermissible bases, as well as any bases or grounds protected by applicable law in each jurisdiction. If you need assistance or an accommodation during the application process because of a disability, it is available upon request. airSlate is pleased to provide such assistance and no applicant will be penalized as a result of such a request. Pursuant to relevant law, where applicable, airSlate will consider for employment qualified applicants with arrest and conviction records.   Read our Recruitment Privacy Notice to Learn how we process your personal information.

Поддерживать и развивать инфраструктуру дата‑платформы: HDFS, Spark, ClickHouse, Airflow и др. Запускать и эксплуатировать Ceph‑кластер с высокими требованиями к SLA.

Задачи: разработка потоков подключения метаданных, автоматизация и шаблонизация подключения СУБД, создание Python-скриптов для генерации DDL/DML и Airflow DAG, оптимизация DAG для таблиц Iceberg, доработка витрины и ведение проектной документации. 🧩 Требования: глубокое знание PySpark и Airflow;

Опыт на должности аналитика в Gamedev от 3-х лет.

— Разрабатывать и поддерживать ETL/ELT-пайплайны на Python. — Интегрировать данные из API, БД, файлов и облачных хранилищ.

Location: Москва Разработки и поддержки витрин данных в Hadoop (HDFS, Iceberg, Hive), ETL - Spark, CTL - Oozie, CI/CD - Jenkins + BitBucket Обязанности • разработка DWH/DataLake/LakeHouse • построение поставки данных в Batch • оптимизация sparkовых джоб • разработка и валидация архитектуры данных Требования • отличное знание SQL (join, CTE, оконные функции, фреймы и др.) • опыт работы с Hadoop, Spark, Iceberg • опыт работы с оркестраторами (например, Airflow) и CI/CD • практическое знание Python (можно Scala) на уровне разработки ETL • практический опыт разработки промышленных ETL потоков Условия * офисный формат работы (Кутузовская) * годовой бонус и ежегодный пересмотр зарплаты * расширенный ДМС с первого дня и льготное страхование для семьи * корпоративный университет Сбера, внутренняя образовательная платформа, участие в IT-конференциях * льготная ипотека в Сбере, корпоративная пенсионная программа, подписка СберПрайм, скидки от партнеров и сервисов группы компаний

Привет, мы ищем продуктового аналитика уровня сеньор в команду на стыке Activation и Growth заниматься ресерчами и аб-тестами (их много, но большинство автоматизированы, а то что еще не автоматизировано - надо автоматизировать).

Location: Almaty, Kazakhstan; Astana, Kazakhstan; Belgrade, Serbia; Cluj-Napoca, Romania; Dnipro, Ukraine; Kharkiv, Ukraine; Krakow, Poland; Kyiv, Ukraine; Larnaca, Cyprus; Lodz, Poland; Lublin, Poland; Lviv, Ukraine; Odesa, Ukraine; Remote.BG, Bulgaria; Remote.GE, Georgia; Remote.KZ, Kazakhstan; Remote.PL, Poland; Riga, Latvia; Sofia, Bulgaria; Tbilisi, Georgia; Varna, Bulgaria; Warsaw, Poland; Wroclaw, Poland; Yerevan, Armenia We are looking for an experienced Senior Snowflake Data Engineer to support the migration of a high volume database and help shape reliable, maintainable data workflows in Snowflake. This initiative is centered on a large scale data platform migration from MySQL to Snowflake. The work includes end to end Snowflake architecture, resilient ingestion pipelines, and improved engineering practices such as CI and CD, testing, and version controlled SQL. Plan and contribute to the migration of data workloads from MySQL to Snowflake Design end to end Snowflake architecture for the target data platform Develop and rebuild ETL and ELT workflows for hundreds of complex sales file formats Build resilient ingestion pipelines for complex, messy, or irregular file formats Contribute to CI and CD, testing, and version controlled SQL practices for data workflows Support performance and cost optimisation across Snowflake workloads Collaborate with cross functional teams on migration, architecture, and delivery activities 3+ years of hands-on Snowflake engineering experience, including tables, stages, file formats, tasks, streams, and Snowflake SQL Hands-on experience with AWS Extensive hands-on experience working with Python Strong understanding of ELT and ETL pipeline design in a cloud data warehouse environment Proven experience migrating data workloads from relational databases such as MySQL or PostgreSQL to Snowflake or a similar platform Experience with data transformation frameworks such as DBT, Airflow, Airbyte, or equivalent Proficiency in SQL and at least one scripting language, with Python preferred Experience working with complex, messy, or irregular file formats and building resilient ingestion pipelines Solid understanding of CI and CD for data workflows using GitHub Actions, GitLab CI, or similar tools Familiarity with data modelling best practices such as Kimball, Data Vault, or staging refined gold patterns

Прогнозировать спроса для товаров без истории продаж («новинок»): разрабатывать модели холодного старта на основе атрибутов товара, категории, характеристик продавца и информации об аналогах.

Писать сложные SQL-запросы для анализа и валидации данных: использовать CTE, оконные функции, сложные JOIN, создавать и изменять объекты базы данных. Разрабатывать хранимые процедуры и настраивать их регулярное выполнение.

Location: Cairo, Egypt Workplace: hybrid inDrive runs many ML models across 48 countries, and the platform under them is growing fast. Teams ship to Databricks, SageMaker and self-managed GPU inference, and LLM workloads are moving into production. We are hiring a Backend Engineer to build and run the services and tooling that ML teams depend on. You will write production-grade Go and Python, own your services from design to on-call, and learn the MLOps stack (Terraform, Kubernetes, Databricks, SageMaker) on the job. You will work with data scientists, ML engineers, and software and data engineers. Key Responsibilities Design, build and run backend services and internal tooling for the ML platform, in Go and Python, and own them in production: delivery, monitoring, on-call. Drive technical research and design for new platform features. Weigh trade-offs and write them down. Build and extend the deployment tooling and SDKs ML teams use to ship models, including serving, autoscaling and data capture. Integrate ML services with the wider backend: sync (HTTP, gRPC) and async (Kafka, RabbitMQ) communication, built for reliability under high load. Support the full ML delivery cycle with data scientists and ML engineers: CI/CD for models and pipelines, monitoring, and model performance metrics. Build observability end to end: metrics, logs, dashboards, tracing, alerting and runbooks. Write infrastructure as code with Terraform and Terragrunt, and ship it through reviewed pull requests. Take part in code and design reviews and help raise the engineering bar. Mentor engineers and hold AI-generated changes to the same review standard as human ones. Skills, Knowledge and Expertise 3+ years of commercial backend engineering, including high-load systems. Strong Go: you know and have used its advanced features in production. Solid Python for data processing (batch, streaming and async) and for service code. Strong SQL: schema design, indexes, query optimisation, and scaling approaches such as replication and partitioning. Experience with Redis or similar key-value stores. Experience with sync and async service communication (HTTP, gRPC, Kafka or RabbitMQ), with a focus on reliability. Docker, CI/CD pipelines, and working Kubernetes knowledge. Experience with AWS/GCP core services (compute, storage, networking, IAM). Understanding of the ML lifecycle: data preparation, model performance metrics, non-functional requirements, and concept drift in production. Familiarity with scikit-learn, PyTorch/TensorFlow. Experience designing and running observability: metrics, logging, dashboards, tracing (Prometheus, Grafana, ELK or similar). Git workflows and code review, comfortable Linux command line, and networking basics (TCP/IP, DNS, VPC, load balancers). Ability to explain a trade-off clearly to a data scientist, a product owner and an on-call engineer. Nice to have: Hands-on experience with MLOps tooling: Databricks (Unity Catalog, Asset Bundles), SageMaker, Kubeflow, MLflow or Airflow. GPU or LLM inference experience (vLLM, Triton, Langfuse). Experience using AI coding agents with full review of generated changes. Experience in emerging markets, or building where there is no playbook yet. Experience with Infrastructure-as-Code (IaC) tools Why join us Real scale and real constraints: models serving a global fleet, GPU budgets that matter, LLM workloads reaching production. A strong backend engineer can grow into the MLOps domain here. We will teach you the platform side; you bring the engineering craft. Help us challenge injustice by creating fair choices for millions of people across 48 countries. Develop your professional skills with access to mentoring, career consulting, and learning programs. Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program. Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects. Work alongside people who take initiative, speak openly, and challenge themselves to grow. Improve your language skills through co-financed courses and internal speaking clubs. Final benefits may vary depending on the location.