Вакансии

Задачи: — WEB, manual/automation, backend/frontend Требования: — Опыт в QA от 3 лет — C# + автотесты (SpecFlow или аналоги) — JavaScript — REST / SOAP / HTTP — MSSQL / PostgreSQL, чтение хранимых процедур — Kafka / RabbitMQ — Понимание Kubernetes и React — Jira / Confluence, Agile / Scrum — Опыт в банковских, кредитных или других сложных интеграционных системах (плюсом) Условия: — Удаленная работа

Задачи: — Отвечать за надёжность, производительность и масштабирование production PostgreSQL-кластеров в 24/7 среде — Влиять на архитектуру и развитие database-инфраструктуры — Заниматься reliability engineering, performance tuning и автоматизацией Требования: — 5+ лет hands-on опыта с PostgreSQL в production — Глубокое понимание PostgreSQL: WAL, VACUUM/autovacuum, locking, memory management — Опыт performance tuning: execution plans, индексы, оптимизация запросов, partitioning — Опыт HA/DR: replication, failover, backup/recovery, Patroni или аналоги — Опыт работы с Linux + Python для автоматизации — Опыт работы с high-load / highly available 24/7 системами — Английский Upper-Intermediate+

Location: Paris, France Workplace: Hybrid We’re a team of experts pushing the limits of what’s possible, united by our common goal to unlock true freedom through digital ownership, making technology accessible for all. We believe in a world where users, creators and enterprises manage their value with ownership and freedom. Our curiosity drives us to innovate, empowering individuals on a global scale. We believe change is constant and our team moves forward as one, with a culture of problem-solving where every employee is empowered and supported to challenge tradition and create solutions. Our mission is simple: to make self-custody accessible and give people the keys to their own financial futures. If you want to make a true impact, we want you to join us at Ledger. At Ledger, we’re proud to be the global platform for digital assets and Web3, with over 20% of the world’s crypto assets secured through our Ledger devices. With our headquarters in Paris, and offices in Vierzon, Grenoble, Montpellier, London, Portland, Geneva, Zurich and Central Singapore, we have a team of around 600 professionals developing a variety of products and services to enable individuals and companies to securely buy, store, swap, grow and manage crypto assets – including the Ledger hardware wallets line with more than 7.5 millions units already sold in 200 countries.   The team To sustain the company's growth, Ledger is looking for a Senior Scala Backend Engineer to join the Cloud Wallet team. This team builds and operates the backend services that power blockchain account management, transaction processing and on-chain data aggregation across all Ledger wallet surfaces, from Ledger Live to Ledger Enterprise, the custody platform used by financial institutions. Every client transaction goes through these services, and so does every new blockchain Ledger supports. We believe in the power of functional programming to build robust and scalable platforms. We take time to share, challenge, and grow together through bi-weekly knowledge-sharing sessions, meetups, conferences, and open source contributions.   What you’ll be doing - Design, build and operate the Scala services that connect Ledger wallets and the Ledger Enterprise platform to blockchain backends: account creation, operation history, transaction crafting, broadcast and lifecycle tracking. - Lead new blockchain integrations end to end, from the integration design (ADR) to production, across account-based, UTXO and privacy-preserving chains. - Own production quality on your scope: SLIs and SLOs, observability, distributed tracing. Take part in the firefighting rotation and drive incidents to resolution, including those raised by institutional clients. - Define clear contracts with the Coin Integration and Ledger Enterprise teams, for example where UTXO selection lives or how transaction states are exposed to consumers. - Write and review ADRs, challenge designs, and raise the bar on code quality, testing (including end-to-end on testnets) and technical debt management. - Work in an AI-native way: use coding agents daily, write the specifications, constraints and verification that let them deliver safely, and contribute to the team's AI tooling. - Mentor other engineers and share your knowledge across the Backend Blockchain community.   You will collaborate cross-functionally with: - The Coin Integration team for coin modules and new blockchain integrations. - The Ledger Enterprise team, main consumer of the team's services for institutional custody. - The Ledger Wallet, Ledger Button and Wallet XP teams as consumers of the team's backend APIs. - The Blockchain platform team for data model alignment, indexing and event streaming. - The Infrastructure and SRE teams for deployment, reliability and scalability. - The Security team (Donjon) for vulnerability analysis and remediation. - The Product team for roadmap prioritization and customer-driven requirements.   What we’re looking for - 5+ years of backend engineering, with real production ownership of distributed systems. - Strong production experience - Interest for Scala and functional programming practice - Hands-on blockchain integration experience: transaction lifecycle, fees, finality, reorgs, on at least two models among account-based, UTXO and privacy-preserving chains. - Experience with event-driven systems and data infrastructure (PostgreSQL, RabbitMQ, NATS or equivalent) and with observability tooling (Datadog or equivalent). - Able to diagnose an incident across several services and own the outcome, from root cause to fix in production. - Comfortable reading and contributing to TypeScript code, as the team works on a polyglot stack. - Daily use of AI coding tools, with a critical eye on their output. - Clear written communication in English, especially for design documents and incident reports. At Ledger, we are dedicated to continually investing in our employees which is why we offer more than just salaries; we provide comprehensive compensation packages that include a wide range of benefits. Here are some of the benefits you can look forward to: - Flexible work options - Our hybrid policy allows employees to work from home up to 3 times per week - Health & Wellness support - Health and Life Insurance. - Financial growth opportunities - Employees can become shareholders in Ledger as well as other financial benefits depending on your country of work. - Commuter allowance - Ledger offers a commuter allowance to contribute to your preferred means of transportation. - Learning & Development - A comprehensive suite of training solutions providing a personalised learning experience for every employee. For regionally specific benefits, your Talent Acquisition contact will be able to provide you with more information. We’re committed to building an inclusive hiring process. If you need any adjustments or accommodations, just let us know, we’ll do our best to support you.

Location: Москва Наша команда занимается амбициозной и стратегически важной задачей – построение единого хранилища данных блока B2C - централизованное и эталонное место хранения данных. Мы ищем сильного инженера данных для работы с крупными распределенными системами. Наш стек строится вокруг экосистемы Hadoop, и мы активно развиваем платформу в сторону Data Lakehouse на базе Apache Iceberg и Data Streamhouse на базе Kafka и Apache Flink. Вам предстоит работать с высоконагруженными системами, потоковой обработкой и участвовать в построении единого хранилища данных компании. Обязанности * **разработка и оптимизация витрин данных:** Проектирование и реализация эффективных **ETL** и **ELT** процессов для витрин данных с использованием **Apache Spark** и **Greenplum**. Обеспечение высокой производительности и отказоустойчивости пайплайнов. * п**отоковая обработка данных (RT и NRT):** Разработка и поддержка стриминговых пайплайнов. Построение NRT пайплайнов обработки данных из **Kafka** в **Apache Iceberg** (**Kafka2Iceberg**) и RT пайплайнов между **Kafka** (**Kafka2Kafka**) с использованием **Apache Flink** и **Spark Structured Streaming**. **Архитектура данных:** * активное участие в разработке архитектуры **решения потоковой обработки данных Data Streamhouse** данных для обеспечения * разработка предложений по развитию **Data Lakehouse**, внедрение best practices работы с **Apache Iceberg** (оптимизация партиционирования, compaction, time travel). **Инженерные практики:** * реализация механизмов **инкрементальной загрузки** данных для минимизации времени обработки и нагрузки на источники * разработка и документирование **API** (на **Java** или **Scala**) для доступа к данным витрин и сервисам семантического слоя **Производительность и качество:** * глубокая **оптимизация Spark-приложений**, включая использование векторного движка **Gluten/Velox** для ускорения рабочих нагрузок * разработка решений для **сверки и контроля качества данных** (**Data Quality**) с использованием **Spark** и **Python**. **DevOps и CI/CD:** Внедрение разработанных решений в продуктовую среду. Настройка пайплайнов сборки и деплоя в **Jenkins**, управление зависимостями задач в **Oozie** или современных оркестраторах. **Коммуникация:** Взаимодействие с внутренними бизнес заказчиками для уточнения и согласования требований, презентация доработок сервиса. Требования ·      Уверенное владение языками: **SQL**, **Java** (или **Scala**), **Python** (как вспомогательный для **ad-hoc** анализа и **DQ**). ·      Глубокое понимание экосистемы **Apache Hadoop** (HDFS, YARN) и опыт работы с ней в продакшене. ·      Опыт разработки пайплайнов на **Apache Spark** (Core, SQL, Structured Streaming) уровня не ниже middle. ·      Опыт работы с потоковыми данными: **Apache Kafka** (как источник/приемник) и хотя бы одним фреймворком стриминговой обработки (**Apache Flink** или **Spark Streaming**). ·      Понимание форматов хранения данных (**Parquet, ORC, Avro**) и современных подходов к управлению Data Lakehouse (обязательно знание **Apache Iceberg**). ·      Опыт оптимизации производительности **Apache Spark** (настройка ресурсов, устранение перекосов данных, работа с memory, оптимизация shuffle). **Будет большим плюсом:** ·      Опыт внедрения промышленных пайплайнов пакетной передачи данных. ·      Опыт внедрения промышленных пайплайнов потоковой передачи данных. ·      Опыт внедрения **Gluten/Velox** или аналогичных векторизованных движков. ·      Навыки сборки и развертывания **Doker**\-образов приложений. Условия * офисный формат работы (м Кутузовская ул. Поклонная 3) * годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * регулярные митапы и развитое DS-community * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбер.

Location: Munich; Warsaw; London Workplace: Hybrid ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. THE ROLE The Inference Foundation team owns the core of Mistral's inference stack: the inference engine and its orchestration, from the feature set and configuration that serve our models in production to the release machinery that keeps the stack current and production-grade. This is a hybrid position spanning production LLM serving, engine and platform development, and capacity engineering. You will work on three intertwined problems: 1. Optimize the inference stack at scale — feature development and fixes in the engine and orchestrator, squeezing more throughput and lower latency out of every GPU under strict quality-of-service targets. 2. Make capacity elastic — scaling up and down should be cheap and fast, not a performance cliff. 3. Power the training of our frontier models — high-performance serving that keeps RL and post-training loops running at full speed.   What You Will Do INFERENCE ENGINE & ORCHESTRATION - Develop and fix the core of the inference stack — engine and orchestrator — including feature selection, configuration, and tuning for maximum performance at scale - Own the release process for the serving stack: validated, regression-free releases through automated performance gates and progressive rollout - Drive improvements and fixes upstream when the open-source engine is the right place for them PERFORMANCE & CAPACITY AT SCALE - Optimize serving efficiency across the fleet — driving down pod startup time, tackling cold-cache regressions on scale-up, smarter caching and offloading - Optimize and maintain the optimal serving topology — overlap communication and transfers with computation, ensure optimal placement, connectivity, and routing SERVING FOR FRONTIER TRAINING - Build the serving infrastructure that powers RL and post-training for our frontier models - Optimize inference performance across the full spectrum of our workloads   What We're Looking For - Experience building and running ML/LLM services at scale, with clear latency and availability targets - Hands-on experience with inference engines such as vLLM, SGLang, TensorRT-LLM, or others - A solid grasp of inference internals: prefill vs. decode, KV-cache behavior, batching, scheduling, speculative decoding, parallelism strategies - Familiarity with distributed and disaggregated serving architectures - Comfortable debugging across the full stack — CUDA/NCCL, kernels, containers, networking, storage - Python for systems tooling and backend services; PyTorch - Kubernetes for running infrastructure at scale - GPU and networking fundamentals: CUDA runtime, NCCL, InfiniBand/RDMA It Would Be Great If You Have - Demonstrated vLLM/sglang know-how — upstream contributions, or a track record of running in demanding production environments - Hardware-aware optimization for various model architectures - Experience serving MoE models at scale (expert parallelism, expert placement/load balancing) - CUDA/Triton kernel development; Nsight Systems/Compute profiling - Rust and/or C++ in production systems WHAT WE OFFER We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link. PRIVACY POLICY Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

Location: AU: Melbourne: (260 Burwood Rd); AU: Sydney (45 Clarence St) Workplace: Hybrid The role As our 'Engineering Manager - Data', you will lead the engineering team responsible for creating and maintaining core models on our data platform. In this role, you will balance strategic leadership with technical delivery, shaping platform architecture while staying close enough to execution to ensure delivery, stability, and quality standards are met. You will empower and grow senior engineering talent, establish operational standards, and manage risk and dependencies across key quarterly initiatives. Partnering closely with product, platform, and security teams, you will ensure high-quality, scalable data products are delivered to solve meaningful customer problems. The team & how they connect The 'Dataverse' team consists of nine engineers located across Australia, New Zealand, and Canada who build and maintain the core models in our data platform. Working across remote locations, the team collaborates closely to manage cross-team dependencies and maintain continuous alignment. They foster an environment grounded in knowledge sharing, constructive feedback, and empathy for stakeholders. Initially, the role may focus on - Designing and building core models on the data platform using AWS and Snowflake infrastructure. - Streamlining real-time data flows and streaming architecture across platform services. - Leading delivery execution for complex data platform initiatives while managing dependencies and risks. - Coaching senior engineers, elevating engineering standards, and supporting team adoption of DataOps practices. Where and how you can work This role offers flexible hybrid working arrangements based out of our Sydney or Melbourne offices. You can balance remote work with designated office boost days to connect, whiteboard, and collaborate in person with your team. Here are some of the things we're looking for, for this role: - A background in data platform delivery alongside data engineering expertise and data modelling capability. - Practical familiarity with cloud infrastructure like AWS and Snowflake, along with an understanding of real-time streaming technologies and data flows. - Your leadership style combines delivery focus with the technical breadth required to guide architectural decisions and step in hands-on when required. - Proven capability in coaching senior engineers, setting clear expectations, and supporting individual career progression. - Demonstrated proficiency managing platform health, establishing Service Level Objectives, and leading operational performance. - Strong collaborative skills to build trusted relationships across product, platform, and security teams. Apply even if your experience isn't a perfect match! At Xero, we hire based on your skills, passion, and the unique perspective you can bring to enhance our culture and team.

Разрабатывать, обучать и внедрять ML/DL-модели для бизнес-задач. Строить AI-агентов и multi-agent workflows с tool/function calling, MCP и LangGraph/LangChain (или аналогами).

🌸Build Android features end-to-end - from technical design to production, with real ownership. 🌸Contribute to mobile product development - primarily on Android, with opportunities to collaborate across both Android and iOS.

Location: Москва ИТ B2C — самая крупная экосистема в Сбере. Нас более 8000 человек в 18 городах России. Мы занимаемся разработкой и развитием розничных решений, помогая сделать сервисы Банка доступнее, безопаснее и удобнее. Мы – команда «Редактируемый профиль клиента». Отвечаем за создание и актуализацию профиля клиента во всех основных каналах (на уровне App/Web-приложения и middle-системы с бизнес-логикой). Наше преимущество – интересные задачи с большим количеством интеграций, от работы которых зависит качество профиля клиента и дальнейшая выдача банковских продуктов. Процессы, которые мы разрабатываем: 1. создание и верификация профилей клиентов физ. лиц сотрудником в офисах и вне офиса банка (в т. ч. с использованием ИИ-распознавания); 2. актуализация персональных данных в приложении для сотрудника на планшетах и стационарных рабочих местах; 3. самостоятельная актуализация данных клиентом в мобильном приложении и веб-версии личного кабинета; 4. создание и обновление профиля клиента от 3-го лица (доверенные лица, представители и т.д.); 5. предоставление сервисов для работы с данными профиля другим продуктовым командам; 6. синхронизация персональных данных с государственными сервисами, интеграция с Госуслугами. Обязанности Чем предстоит заниматься \- Поддержка и развитие CI/CD пайплайнов в Jenkins \- Контейнеризация приложений, развитие микросервисной архитектуры \- Участие в настройке и администрировании кластеров Kubernetes \- Реализация доставки приложений по GitOps-модели с помощью ArgoCD \- Управление чартами: создание, обновление и деплой Helm-чартов \- Базовые задачи по Linux-серверам \- Мониторинг базовых метрик \- Взаимодействие с командой разработки. Требования Требования \- Базовые знания Linux \- Понимание устройства сетей (DNS, HTTP/HTTPS, порты) \- Практическое знакомство с Docker \- Опыт написания скриптов автоматизации на ansible, groovy/python \- Уверенная работа с системами сборки на Maven/Gradle \- Базовое знание Kubernetes(must) \- Опыт траблшутинга сервисов на Java и OS Linux \- Понимание DevOps-практик и CI/CD. \- Понимание практики GitOps Условия * офисный формат в Москве (Кутузовский пр, 32) * ежегодный пересмотр зарплаты и годовая премия * расширенный ДМС и льготное страхование для семьи * уникальная система обучения Сбера для профессионального и карьерного развития * выгодная ипотека для сотрудников * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера * корпоративная пенсионная программа.

Location: Europe Workplace: Remote ABOUT ENEBA At Eneba, we’re building an open, safe and sustainable marketplace for the gamers of today and tomorrow. Our marketplace supports close to 20m+ active users (and growing fast!), provides a level of trust, safety and market accessibility unparalleled to none. We’re proud of what we’ve accomplished in such a short time and look forward to sharing this journey with you. Join us as we continue to scale, diversify our portfolio, and grow with the evolving community of gamers. ABOUT THE ROLE We’re looking for an Analytics Engineering Lead to set the direction for analytics engineering at Eneba and help the team deliver trusted, business-ready data. You’ll lead a team of around 10 analytics engineers. You’ll define what analytics engineering owns and where it is going, connect the team’s work to real business needs, and ensure curated data can be used directly by business teams, BI tools, and AI agents without an analyst in between. WHAT YOU’LL DO - Create and own the analytics engineering strategy, direction, and goals. - Lead, coach, and develop a team of around 10 analytics engineers through regular feedback, one-to-ones, performance management, review cycles, and hiring, helping them become increasingly autonomous. - Establish the analytics data-modeling architecture, engineering principles, quality standards, and ways of working that guide how the team builds and maintains models. - Ensure analytics engineering work begins with a clear understanding of the business problem and delivers data that people can use with confidence. - Enable reliable self-service analytics by making curated data usable by business teams, BI tools, and AI agents, with appropriate semantic layers, metrics, discoverability, and governance. - Evaluate the current analytics landscape and make pragmatic, evidence-based decisions about what should be improved, stopped, rebuilt, or delegated. - Partner directly with stakeholders across the company, including teams such as Accounting, Payments, and Marketing, as well as senior leaders. - Build a strong understanding of Eneba’s business and translate varied stakeholder needs into a clear direction for the team. - Identify and empower technical experts within the existing team to help define and implement engineering standards. - Evaluate analytics tooling and delegate technical discovery where appropriate, while retaining final ownership of analytics engineering architecture and tooling decisions. REQUIREMENTS - Proven experience leading and developing data, analytics, or analytics engineering and people-management in product-focused tech companies. - Experience defining strategy and setting direction for analytics engineering, data modeling, or a closely related function. - A track record of establishing data-modeling architecture, principles, or standards that guide how other engineers build and maintain models. - Strong business judgment: you focus on why data is needed and what problem it solves, not only how models are built. - Strong stakeholder management and communication skills, with proven experience partnering closely with product leadership and representing data in executive-level and cross-functional discussions. - Ability to form clear, evidence-based opinions, challenge existing approaches, and lead change under sustained pressure including recommending that work be stopped or rebuilt when needed. - Experience operating in a high-growth, fast-moving organization with low approval layers, where data supports many business functions. Scale-up experience is helpful. - Working knowledge of SQL and data modeling, hands-on experience with DBT, and familiarity with at least one BI or dashboarding tool. - Fluent English (business proficiency required) What it’s like to work at Eneba - Opportunity to join our Employee Stock Options program. - Opportunity to help scale a unique product. - Various bonus systems: performance-based, referral, additional paid leave, personal learning budget. - Paid volunteering opportunities. - Work location of your choice: office, remote, opportunity to work and travel. - Personal and professional growth at an exponential rate supported by well-defined feedback and promotion processes. - We’re an international team and our business language of choice is English. Good English level is required, proficiency is preferred. Please submit your resume in English - To find out about how we handle your personal data, make sure to check out our Candidate Privacy Notice https://www.eneba.com/candidate-privacy-notice   #LI-LK1

Эксплуатация и тюнинг СУБД: Полный контроль над состоянием, профилированием и оптимизацией PostgreSQL под конфигурацию серверов в AWS; Отказоустойчивость и HA/DR: Настройка и сопровождение репликации, Patroni, PgBouncer, бэкапирования и катастрофоустойчивости;

- Превращать телеметрию автономного транспорта в данные, удобные для анализа: разрабатывать и запускать Spark-пайплайны, продумывать преобразования и проверки качества. - Подключать новые источники данных: разрабатывать интеграции с различными системами и обеспечивать надёжную загрузку данных в хранилище.

Задачи: — Дизайн и построение LLM пайплайнов, AI агентов и RAG-систем с функциями вызова инструментов и структурированным выводом (JSON schema) — Разработка production-grade Python сервисов с использованием Django, Celery, Redis — Валидация продуктовых гипотез на основе реальных данных и аналитики — Измерение и оптимизация качества, производительности и стоимости LLM — Тесное сотрудничество с продакт-менеджерами и инженерами для превращения идей в масштабируемые решения Требования: — Опыт создания production LLM приложений — Практические знания RAG, embeddings, векторного поиска, prompt engineering и оценки LLM — Опыт работы с LangGraph, LangChain или аналогичными фреймворками — Сильный опыт Python backend (Django, PostgreSQL, асинхронное программирование) — Навыки работы с SQL и Pandas, ориентированность на данные — Владение английским языком на уровне B2-C1, русским на уровне C1 — Локация: любой город, кроме России и Беларуси Будет плюсом: — Опыт работы с векторными базами данных (Qdrant, pgvector и др.) — Опыт дообучения, классификации текста или работы с embedding моделями — Опыт с Kubernetes, gRPC или Go Условия: — Удалённая работа — Гибкий график — Профессиональное развитие Компания Mad Devs занимается решением сложных задач и созданием инновационных решений в области AI, проекты охватывают Европу, США и Юго-Восточную Азию. Вакансия направлена на революцию в юридической индустрии с помощью AI.

In this role, you will work on the development and continuous improvement of Sophea AI, expanding our LLM capabilities across European languages, with a particular focus on Polish. You will be deeply involved in LLM pre-training, training from scratch, fine-tuning, model evaluation, inference optimization, and production-grade ML systems.

-Разрабатывать автоматизированные тесты для веб-сервиса (функциональные тесты api/ui, визуальное тестирование, нагрузочное тестирование). -Поддерживать автотесты (6000+ api, 600+ ui).

Чем предстоит заниматься: — Развивать Kubernetes-инфраструктуру в облаке и на bare-metal.

Location: London, United Kingdom - Deliveroo Our Global Structure Deliveroo is now part of DoorDash, bringing together teams with even greater reach, scale, and ambition. Depending on your role, you may collaborate with teammates, systems, and leaders across DoorDash and Wolt. Together, we’re unlocking new possibilities as one global team. Senior Software Engineer, GenAI Platform About the Team Deliveroo's GenAI Platform team sits within Machine Learning Platform and builds the shared infrastructure that helps DoorDash, Wolt, and Deliveroo teams safely bring GenAI-powered products, agents, automation, and personalisation to production. Our mission is to increase the velocity of business impact from GenAI. A central pillar of that work is running frontier open-weight LLMs and VLMs (such as GLM, Qwen, Kimi, and DeepSeek) ourselves — real-time GPU serving, high-throughput batch inference, and fine-tuning on autoscaling GPUs — delivering large cost and latency wins (for example, a billion embeddings produced roughly 20× cheaper and visual models served roughly 72% cheaper). We also own core platform surfaces including the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution. About the Role You will join a small, high-leverage team building production infrastructure for Generative AI at Deliveroo and DoorDash, leading the design and architecture of our open-weights model platform spanning inference and fine-tuning: real-time GPU serving, high-throughput batch inference, and model fine-tuning. You’ll set technical direction across model serving and inference engines, fine-tuning and training pipelines, GPU autoscaling and utilization, batch pipelines, backend services, and observability, and mentor engineers as you go. This role is ideal for a senior engineer who enjoys owning ambiguous, high-impact systems and pushing the cost/performance frontier of GPU inference and fine-tuning in a fast-moving technical area where product needs, model capabilities, vendor ecosystems, and cost/performance tradeoffs are evolving quickly. You’re excited about this opportunity because you will… Lead the design of infrastructure that helps teams move GenAI ideas from prototype to production, increasing the velocity of business impact from AI across the company. Own and evolve our open-weights serving stack — real-time GPU endpoints, high-throughput batch inference, and fine-tuning (SFT/DPO/LoRA) — alongside the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution. Architect scalable, high-performance systems for model serving, batch inference, GPU autoscaling, and fine-tuning that power real customer and internal automation use cases Push the cost and latency frontier of GPU inference — turning batch jobs that took days into hours and cutting inference cost by multiples — while giving product teams a clean choice across open-weight and closed-source models with reliability, fallback, observability, and cost controls built in. Build platforms that support rapid experimentation while meeting production standards for latency, scale, monitoring, SLOs, playbooks, and operational excellence. Partner closely with — and raise the technical bar for — ML engineers, product engineers, data scientists, and platform teams across DoorDash, Wolt, and Deliveroo to turn emerging GenAI capabilities into durable platform primitives. Set technical direction for the future of the company's centralised GenAI platform — including emerging directions such as reinforcement learning (RLHF/RLVR), agent optimisation, and other post-training and agentic techniques — enabling the next generation of AI-powered products, agents, automation, and personalisation. We’re excited about you because… BSc, MSc or PhD in Computer Science or equivalent 5+ years of industry experience in software engineering Deep backend engineering fundamentals, especially in Python and distributed systems. Track record of designing and owning production services, APIs, data pipelines, or ML infrastructure at scale. Experience operating systems in production, including observability, debugging, reliability, incident response, and performance/cost optimization. Deep hands-on experience with LLM inference and/or fine-tuning of open-weight models in production — serving (latency, throughput, batching, autoscaling, GPU utilization) and/or fine-tuning (SFT/DPO/LoRA). Demonstrated technical leadership: leading design across ambiguous, fast-moving technical areas, mentoring engineers, and turning customer use cases into reusable platform capabilities Proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software Nice To Haves Experience with LLM inference engines and serving frameworks (e.g., vLLM, SGLang, TensorRT-LLM) in production Experience with distributed/multi-node fine-tuning and training pipelines (SFT, DPO/RLHF, LoRA), including data preparation and evaluation GPU performance work — multi-node/distributed inference, KV-cache/memory optimisation, quantisation (FP8/INT8/AWQ/GPTQ), or cold-start/throughput tuning Experience with Kubernetes, cloud infrastructure (AWS/GCP), GPUs, serverless/elastic GPU platforms (e.g., Modal), or high-throughput batch systems Experience with LLM gateways, model routing, vendor abstraction, or cost attribution Experience building developer platforms, internal platforms, or self-serve infrastructure Experience building and deploying AI agents or MCP servers in production Experience with eval systems, LLM observability, tracing, RAG, search, or vector databases Diversity, Equity and Inclusion At Deliveroo, we know that a great workplace reflects the world around us and that true diversity and inclusion make us stronger, more creative, and better at what we do. We’re committed to fostering an environment where everyone can do their best work and feel they belong. We believe in equality of opportunity and welcome candidates from all backgrounds regardless of age, gender, ethnicity, disability, sexual orientation, gender identity, socio-economic background, religion, or belief. If you have a disability or long-term health condition and need support to apply for one of our roles, or require any reasonable adjustments during the recruitment process, you’ll have the opportunity to let us know once you’ve submitted your application. We’ll share details on how to request support so we can ensure you have a fair and equitable experience. If you’re excited about making a real impact in a fast-moving marketplace and growing your career alongside ambitious, supportive teams, we’d love to hear from you! Beware of recruitment scams: DASH Brands will never ask you to pay money or share sensitive financial information during hiring — learn more about DASH Brands' legitimate recruiting process at careersatdoordash.com/recruitment-scam-awareness .

Основной фокус роли — связка native-платформ iOS/Android с Flutter: работа с native APIs, embedders и platform adaptation code, то есть скорее глубокая работа на стороне iOS/Android и интеграции этих компонентов с Flutter.