Вакансии

Задачи: — Проводить функциональное, интеграционное, регрессионное и End-to-End тестирование. — Тестировать Web-приложения на уровнях Frontend и Backend. — Проводить API-тестирование с использованием Postman, Insomnia или Swagger. — Анализировать требования, разрабатывать тест-кейсы и чек-листы, оформлять баг-репорты. — Тестировать сложные бизнес-процессы, статусные модели и интеграции с внешними информационными системами. — Проверять корректность обработки запросов, авторизации и передачи данных между сервисами. — Работать с базами данных PostgreSQL, выполнять SQL-запросы и проверять корректность данных. — Анализировать логи для выявления причин возникновения ошибок. — Взаимодействовать с разработчиками, аналитиками и другими участниками команды для устранения дефектов. — Участвовать в планировании спринтов и совершенствовании процессов обеспечения качества ПО. Требования: — Опыт работы от 3 лет. — Уверенное знание теории тестирования, техник тест-дизайна и жизненного цикла разработки и тестирования ПО (SDLC/STLC). — Опыт функционального, интеграционного, регрессионного и E2E-тестирования. — Опыт тестирования Web-приложений (Frontend и Backend). — Понимание REST API, HTTP/HTTPS, методов запросов, статус-кодов, заголовков и механизмов авторизации. — Уверенное владение Postman, Insomnia или Swagger. — Знание SQL и опыт работы с PostgreSQL: SELECT, JOIN, GROUP BY, фильтрация и сортировка. — Опыт работы с DBeaver или аналогичными инструментами. — Уверенное использование DevTools для анализа работы Web-приложений. — Базовые навыки работы с Linux и терминалом. — Понимание принципов работы SSH, VPN и SSH-туннелей. — Опыт анализа логов через Kibana или аналогичные системы. — Базовое понимание Docker, Kubernetes, CI/CD и процессов развёртывания приложений. — Аналитическое мышление, внимательность к деталям и системный подход к поиску дефектов. Будет преимуществом: — Опыт работы с ЭЦП и NCALayer. — Понимание криптографических алгоритмов RSA, ГОСТ и механизмов хеширования SHA-256. — Опыт тестирования интеграций с государственными информационными системами и внешними сервисами. — Понимание микросервисной архитектуры. — Навыки автоматизации тестирования. — Базовые знания Git и одного из языков программирования: Python, Java или JavaScript. Личные качества: — Ответственность и внимательность к деталям. — Аналитическое и критическое мышление. — Умение работать в команде. — Самостоятельность в решении задач. — Грамотная коммуникация с разработчиками и аналитиками. — Готовность разбираться в сложных технических вопросах. Условия: — Офис — г. Астана, пр. Мәңгілік Ел, 55/14. — Полная занятость. — График работы: 5/2, с 08:00 до 17:30. — Лечебное пособие к ежегодному отпуску — 2 оклада. — Аванс в середине месяца. — Возможные премии по праздникам и итогам года. — Материальную помощь к памятным датам. — Комфортабельный офис в центре Астаны. — Утреннюю и вечернюю развозку по городу. — Возможность работать над государственными информационными системами и масштабными интеграционными решениями.

Location: Worldwide We are looking for Lead QA Engineer to lead and improve our ATM testing processes, ensure the quality of our web applications through manual and automated testing across UI and backend, and own the full testing lifecycle — from test design and documentation to defect tracking and quality monitoring. Challenges that await you: Functional acceptance, exploratory, regression, and smoke testing of web applications Leading and improving ATM testing processes Design, implementation, and execution of the full software testing lifecycle Development and maintenance of test documentation Organizing and coordinating ATM testing activities at all stages Acceptance, regression, and smoke testing (both automated and manual) Monitoring the quality and effectiveness of ATM testing processes Identification, localization, and reporting of defects using a bug tracking system Establishing and optimizing testing processes and implementing consistent testing approaches and standards What makes you a great fit: Experience and understanding in Web UI testing; Experience in programming with TypeScript/Java/Python Experience and understanding in backend testing Experience in developing and maintaining test documentation Experience working with Git or other version control systems Experience in developing and maintaining automated tests Experience working with CI/CD tools B1 or higher English level for effective communication with an international team. Your bonus skills: Experience working with gRPC and Protocol Buffers (Protobuf) Experience working with Kafka Experience in leading QA teams and testing processes Our ways of working: Innovative Spirit: A commitment to creativity and groundbreaking solutions Honest Feedback: valuing open, transparent communication Supportive Team: a strong, collaborative community Celebrating Achievements: recognizing our wins together High-Tech Environment: a team full of smart and revolutionary people who date to challenge the status quo of incumbent finances Our benefits: Relocation support to our hub — Cyprus with assistance for the employee and their family Flexible work from one of our offices or remote Healthcare Coverage Education Budget: Language lessons, professional training and certifications Wellness Budget: Mental health and fitness activity reimbursements Vacation policy: Annual leave and paid sick leave as per applicable policies

Проект: Финтех Требования: — Знание языка программирования Python — Опыт/навык разработки BDD-тестов с применением Cucumber и аналогов — Знания и умение применять техники тест-дизайна и тест-анализа — Опыт тестирования backend приложений — Навык написания SQL запросов с применением подзапросов, группировок — Опыт локализации и анализа дефектов — Знакомство со стеком ELK (Kibana) — Опыт оценки покрытия тестами функциональностей системы — Знания подходов TDD, BDD, DDT — Понимание архитектуры web-приложений — Знания и опыт ведения тестовой документации (тест-кейсы, тест-планы) Условия: — Официальное трудоустройство; оплата больничного, отпуск 28 календарных дней — Возможность карьерного роста — Возможность участия в интересном проекте — Опыт работы в команде профессионалов

Location: Anywhere in France, Belgium, Spain HEALTH CAN’T WAIT. Not for symptoms to get worse. Not for a six‑month appointment. Not for a system to catch up. But that’s exactly how healthcare works today. You wait, until you can’t. Alan exists to end the wait. Health is a universal right, and we believe this right can only become real when it’s coupled with prevention. We need to stop treating health as something we repair and start treating it as something we build, every day. It’s not solely a question of willpower. It’s the healthcare system itself that needs to work for everyone, in a sustainable way. So we are building the new standard in prevention insurance. Alan is the first company that integrates insurance, prevention, and care into a single, acclaimed user experience. We are on an incredible journey to build a global leading company, with a unique culture https://alan.com/en/careers. We already partner with 40K+ companies of all sizes, serving more than 1M+ members, and have reached €800M+ in ARR. Prevention as the new norm. That's what we're building with our team of 1000+ people. If it speaks to you: we're hiring across France, Spain, Belgium, and Canada. And beyond. 🐼 WHO WE ARE You. Better. With Alan. Alan's vision is to make prevention the new norm of care for all. Our mission is to help people live in good health to 100 while helping employers feel proud, turning health benefits from a cost center into their most valuable investment. We're building a vertically integrated health partner that seamlessly unites insurance and smart healthcare delivery into one system. By connecting all aspects of care - private, public, and direct to consumer - we create the most member-centric healthcare experience. Through deep engagement, we empower everyone to overcome day-to-day health obstacles and live healthier lives. We partner with 35K+ companies of all sizes, serving more than 1M+ members, and have reached 770M+ in ARR. Our team of 700 people (still growing) operates across France 🇫🇷, Spain 🇪🇸, Belgium 🇧🇪, and Canada 🇨🇦. 🚀 WHAT YOU'LL BUILD AND WHO YOU'LL WORK WITH You would join the product team that builds the AI agents answering our 1M+ members' questions about their health insurance - reimbursements, coverage, claims, account changes and more - instantly and without waiting for a human. Our AI agents already fully resolve more than 50% of members' questions. Your mission is to push that number further, increase the quality of answers and the trust members place in them, and make our agents work with the other AI agents at Alan, such as Mo our health companion. Concretely, you will: - Design and ship LLM-powered agents that understand a member's request, reason over their contract, claims and history, and take real actions on their behalf - building the tools, retrieval and context pipelines that give them the right information at the right moment - Make our agents interoperate with other AI agents across Alan, such as Mo: handing off conversations, sharing context and calling each other's capabilities, so members get one seamless experience - Bring automated resolution to new channels, starting with AI phone support: real-time voice conversations with the same quality bar as written ones - Own evaluation: build datasets, automated evals and quality monitoring so we know an agent is right before members see it - Measure and improve resolution rate, answer quality and member satisfaction in production, iterating fast on prompts, models and architecture - Work hand in hand with care experts and product to identify the next topics the AI can fully resolve ⚡ WHY THIS ROLE IS SPECIAL - Direct Impact: Every point of automated resolution means members get help instantly, when they need it the most - and directly drives Alan's scaling, growth and profitability. - Applied AI at scale: You won't build demos. You'll ship AI agents that handle hundreds of thousands of real conversations per month in a regulated, high-stakes domain, where accuracy truly matters. - Frontier problems: Agentic workflows, tool use, evaluation, safety and guardrails, cost and latency trade-offs - you'll tackle the hardest open questions in production LLM systems. - Ownership & Growth: Influence product direction, mentor other engineers, and lead technical initiatives that shape the future of healthcare access. 🔍 WHAT WE'RE LOOKING FOR Technical Requirements: - 5+ years building scalable software systems - Strong backend experience (Python is a plus) - Hands-on experience building products with LLMs (agents, tool calling, RAG, prompt engineering, evaluation) - ideally shipped to production - An evaluation mindset: you know how to measure whether an AI system is actually good, and how to make it better What we value: - You love turning complex problems into elegant solutions - You care about the "why" behind what you build, not just the "how" - You stay on top of a fast-moving AI landscape and are pragmatic about what really works - Humble and still willing to grow (no matter your seniority) Mission Alignment: - Healthcare technology excites you - You understand that insurance isn't just about money - it's about access to care But above all, we are looking for high potential and curiosity: make sure to show us this when you apply! Everything else is a bonus. ⭐ THE ENGINEERING TEAM ⭐ In our engineering team, we build the infrastructure, interfaces, and applications to provide first-class service to our members, health professionals, and even ourselves! Being an engineer at Alan means joining a team of talented, committed and passionate engineers, with a lot of product interaction. We move fast, with a lot of ownership, and are proud to tackle big problems! Our process is very simple: those who make product decisions are the same ones who build them. Our stack is React, Python/Flask, PostgreSQL in a monorepo. If you are not familiar with this stack but are a keen learner, it is totally fine. We deploy daily and believe in distributed ownership - you build it, you own it. Want to know more? Read this article https://blog.alan.com/tech-et-produit/engineering-organisation-at-alan if you want to discover how life as an Engineer at Alan and this article https://medium.com/alan/engineering-career-path-at-alan-e5de5c2e740c on our Engineering career path. To see all our opened Engineering positions, click here https://jobs.ashbyhq.com/alan?departmentId=8ba0fb44-ec79-4731-bf45-d65a821e24f0 ! 🌍 HOW WE WORK Location: You must be legally eligible to work in France, Belgium or Spain. Remote work: We offer remote work flexibility, but we value in-person collaboration. Check here https://alan.com/en/careers/a/everyday-life for more information about our everyday life! 🎯 IMPORTANT NOTE: WE HIRE PEOPLE, NOT ROLES. If you're excited about this opportunity but don't check every box, we'd love to hear from you. Everyone, no matter how underrepresented, should feel free to apply, as it can only bring learnings or success. If you identify yourself as a woman: Did you know that research shows women often apply only when meeting 100% of requirements? Remember, this is just a guide, not a checklist. We'll be thrilled to receive your application! 🔖 Check out our About Alan and Career pages, as well as our Medium, blog and Glassdoor pages for more info. YOU WANT TO KNOW MORE ABOUT ALAN? - 🙌 Perks & Benefits https://alan.com/en/careers/a/salary-equity: Alaners are provided with a stimulating environment and perks ensuring they are happy, efficient and spend only high-quality time with co-workers. - 🤘 A strong culture https://alan.com/en/about/a/leadership-principles: People joining Alan are often surprised and delighted by our innovative working method. We have a set of cultural values that guide our approach to work 🔄 A quick note about the process Note: While you're applying for a specific area, you may join a different engineering team based on where we think you'll have the most impact. Check out descriptions of other areas here https://www.notion.so/alaninsurance/Software-Engineering-Areas-Alan-2c61426e8be7805bacd2cdbc08c32640?source=copy_link.

Требования: — Руководил командой разработки и может рассказать, как помогал ей достигать результата — Самостоятельно проектировал, запускал и сопровождал backend-сервисы — Умеет сравнивать технические решения и объяснять их преимущества, ограничения и стоимость поддержки — Обсуждает продуктовые задачи с коллегами и помогает договориться о приоритетах О компании: VK AdBlogger — внутренний стартап, который помогает рекламодателям находить покупателей через контент авторов, а авторам — зарабатывать на рекламе и рекомендациях.

Задачи: — Разработка новых и поддержка существующих фич в SSG и SSR приложениях на Vue 3 / Nuxt 3 — Проектирование и реализация переиспользуемых компонентов, работа с Composition API и TypeScript — Интеграция с REST API: работа с запросами, обработка ошибок, типизация ответов — Оптимизация производительности: lazy loading, code splitting, оптимизация рендеринга — Участие в code review, декомпозиция задач, взаимодействие с backend-командой при проектировании контрактов API — Разработка и поддержка технической документации, написание unit-тестов для компонентов и логики Требования: — Опыт коммерческой разработки на Vue 3 и Nuxt 3 от 2 лет — Уверенное знание Composition API, Options API — TypeScript — уверенное владение: типизация компонентов, props, emit, API-ответов — Понимание SSR / SSG в Nuxt: разница между серверным и клиентским рендерингом, useAsyncData / useFetch — Управление состоянием: Pinia (предпочтительно) или Vuex — Понимание реактивности Vue — HTML, CSS, адаптивная вёрстка — Опыт работы с Git в команде (PR, code review, branching) — Опыт с Vitest / Jest для тестирования компонентов — Понимание Web Vitals и оптимизации производительности — Знание Docker, базовый CI/CD — Опыт работы с дизайн-системами (Figma → код) Условия: — Полная занятость — График: 5/2 — Формат работы: удалённо — Зарплата до 185 000 ₽ за месяц, на руки

Location: New York, NY, United States About Us Fanatics is a leading global sports platform redefining how fans connect with the teams, players and moments they love. Our businesses — Fanatics Commerce, Fanatics Collectibles, Fanatics Betting & Gaming, Fanatics Events, Fanatics Advertising, Fanatics Markets and Fanatics Studios — maximize the presence and reach for hundreds of partners globally and create a one-stop experience that allows fans to buy, bet, collect, and earn, while engaging with sports in deeper, more immersive ways. This includes licensed fan gear and lifestyle products, sportsbook and iGaming, physical and digital trading cards, live events, premium sports entertainment content, credit card offerings and more. Together, they form a powerful, integrated ecosystem — connected by the Fanatics ONE loyalty program and FanCash, the currency of sport — delivering personalized experiences to over 100 million fans worldwide. Fanatics holds partnerships with over 900 sports properties, from professional leagues and players’ associations to teams, colleges and retail partners; over 7,000 athletes and celebrities; and over 2,000 retail locations, including all Lids stores. Backed by a team of more than 22,000 employees, Fanatics is committed to relentlessly enhancing the fan experience. Overview Fanatics Betting & Gaming (FBG) is on a mission to create the best online sportsbook in the industry. We are seeking an Engineering Manager to lead the team that builds the Balance and Ledger platform: the shared ledger and customer-level balance that holds customer funds across Fanatics. This leader will own systems where accuracy matters most, guiding the team through complex and ambiguous problems while ensuring operational excellence, consistent delivery, and long-term impact. The team sits within the Customer, Accounts & Transactions (CAT) domain and works closely with the Payments team. The Engineering Manager plays a central role in shaping the architecture, processes, and engineering culture of the balance platform. You will lead backend engineers (Java, Spring Boot, AWS) who are responsible for the ledger, the rules that decide where each portion of a balance can be used, and the migration of existing wallets onto a shared platform. At FBG, we're revolutionizing sports betting with cutting-edge technology, a customer-first mindset, and a world-class engineering team. You'll have the opportunity to work on complex, high-scale challenges in an exciting and rapidly growing industry, as part of a team that values innovation, integrity, and excellence. Responsibilities Operational Leadership & Ownership Lead an engineering team that owns the ledger and balance services for customer funds, where correctness, auditability, and availability are key. Drive operational excellence by setting high standards for security, reliability, observability, and on-call practices. Lead the phased migration of existing business wallets onto the shared ledger, including reconciliation, cutover, and rollback planning that avoids customer-visible downtime. Partner with Finance, Legal, and Compliance on fund attribution between businesses, settlement, chargebacks, and audit requirements. Identify and resolve technical and operational risks before they affect the business. Team Performance & Delivery Build and maintain a high-performing engineering team through hiring, coaching, performance management, and career development. Define and execute a roadmap that balances new capabilities, migration work, technical health, and operational load. Plan team capacity honestly, make tradeoffs visible, and hold the team to the commitments it makes. Deliver maintainable, scalable software that handles high transaction volume without losing or double-counting a transaction. Stay close enough to the technical work to review designs, take part in architectural decisions, and unblock engineers. Communication, Stakeholder Management & Product Partnership Communicate clearly with engineers, Product, Finance, Legal, Compliance, and executive stakeholders. Build partnerships with the Payments team and with the teams in other Fanatics businesses that depend on the balance, and agree on ownership boundaries. Write and present clear documentation, including roadmaps, vision documents, design reviews, and incident reviews. Represent the balance platform's technical perspective in broader company planning. Navigating Complexity & Ambiguity Lead the team through open questions that span engineering, finance, and regulation, using structured and data-informed approaches. Bring Product, Legal, Finance, and Engineering viewpoints together to make decisions that hold up over time. Provide clarity as requirements change, new businesses onboard, and regulatory expectations evolve. Travel Must be open to occasional travel to Fanatics Betting & Gaming offices and other locations for conferences, events, meetings, and team building activities. Qualifications We value aptitude and integrity over years of experience, but here are key skills that will make you successful in this role: 3-5+ years of experience managing software engineering teams, with a track record of delivering secure, scalable systems in regulated or high-stakes environments. Background leading teams or contributing in a Java and AWS ecosystem preferred. Experience with ledgers, wallets, balance services, payments, or other financial systems of record is highly desirable. Understanding of transactional consistency, idempotency, and data modeling in systems where accuracy cannot be compromised. Experience with event-driven architectures and message streaming platforms such as Kafka. Experience leading migrations of live, high-traffic systems with minimal customer impact. Ability to balance strategic direction with technical depth. Strong communication skills and the ability to influence across Engineering, Product, Finance, Legal, and Compliance. Experience working through complex, cross-functional, and regulatory challenges. Commitment to operational excellence and to building systems that people can trust. Passion for mentoring and developing engineers, and for fostering a culture of accountability, empathy, and innovation. Depending on the role, your interview and onboarding experience may include in-person components, such as onsite interviews or Launching into Better: LIVE—a multi-day cultural immersion in New York City for full-time, non-seasonal hires. These sessions are designed to build connection and bring our culture to life, though specific travel and participation requirements will be confirmed based on your role and location. Your recruiter will provide clear guidance at each stage of the process. For information about our benefits, please visit https://benefitsatfanatics.com/ Ranges will change based on country and state of residence, which are reflected in Geographical Zones defined by Fanatics Betting and Gaming. The range incorporates all of our Geographical Compensation Zones and is subject to change as the Zone associated with the actual offer is confirmed. In addition to the base and bonus, full-time employment, and more. For information about our benefits, please visit https://benefitsatfanatics.com/ Salary Range $190,000 — $237,500 USD By submitting your application, you agree to our terms of service and acknowledge you have read our Candidate Privacy Policy. Fanatics will never ask candidates for money, banking information, or other sensitive personal information as part of the interview process. If you receive a suspicious message claiming to be from Fanatics recruiting, please do not respond and report it here .

Задачи: — Design, develop, and optimize production-grade agentic AI solutions. — Run meaningful evaluations to improve prompts, model behavior, orchestration, reliability, and performance in production. Требования: — Deep expertise in machine learning and applied AI. — Experience building and deploying production-level AI/ML services. — Strong software engineering and coding skills. — Experience developing or optimizing LLM- and agent-based applications. — Strong knowledge of prompt engineering and optimization. — Strong knowledge of model evaluation and benchmarking. — Strong knowledge of AI quality, reliability, and performance measurement. — Strong knowledge of agent behavior and orchestration. — Ability to design and run meaningful evaluations and interpret results. — Experience with APIs, data pipelines, backend systems, and AI platforms. — Familiarity with modern AI tools, frameworks, and cloud environments. — Ability to collaborate with platform engineers, product teams, and business stakeholders. — Strong communication skills and experience delivering maintainable, production-quality solutions. Плюсом будет: — Python experience. — Background as an Applied AI Scientist, AI Engineer, or Machine Learning Engineer. Условия: — Own a defined production AI workstream with direct impact on reliability and scale. — Work closely with an existing platform engineering team on applied AI delivery. — Focus on production-grade AI implementation rather than purely research-oriented work.

Задачи: — Создание MVP и дальнейшая поддержка ИИ-продукта на стыке соматических практик и голосовых технологий. — Разработка Telegram-бота / Mini App с голосовым ИИ-аватаром. — Реализация голосового стриминга через OpenAI Realtime API. — Клонирование голоса и управление паузами через ElevenLabs. — Обеспечение мультиязычности: RU/KZ с перспективой глобального масштабирования. — Backend на Python/Node.js, PostgreSQL. — Интеграция платежей Kaspi Pay / CloudPayments. Требования: — Опыт работы с OpenAI Realtime API, аудиопотоками и голосовыми ИИ-системами. — Техническое ТЗ полностью готово. Условия: — Формат: проектная разработка MVP + дальнейшая поддержка.

Задачи: — Разработка backend реализаций для веб-сервисов — Участие в разработке архитектуры и реализации прототипов новых веб-сервисов — Коммуникация с front-end и back-end разработчиками, QA — Написание валидного читабельного кода — Написание юнит-тестов для разработанных компонентов Требования: — Гражданство и локация только РФ — Опыт в автоматизации на Python, не менее 4 лет — Владение pytest, pytest-asyncio — Опыт с api тестами (REST) и UI тестами (Selenium, Playwright) — Знание базово: SQL, Redis, MongoDB (pymongo), Clickhouse/Postgresql, Kafka, SFTP, Minio — Опыт работы с Docker и docker-compose — Опыт работы с Allure TestOps — Навык использования моков — Знание CI Условия: — Удалённая работа — Занятость full-time — Уровень зарплаты до 146к рублей на руки

Задачи: — Разработка новых сервисов и внутренних инструментов — Создание и доработка функционала существующих проектов — Разработка backend-логики и API — Работа с базами данных и интеграциями — Разработка frontend-интерфейсов — Поиск и исправление ошибок, оптимизация кода — Работа над несколькими проектами параллельно — Использование AI-инструментов для ускорения разработки Требования: — Умение работать с AI-инструментами, проверять сгенерированный код, проводить тестирование и доводить задачи до рабочего результата Будет плюсом: — Опыт коммерческой разработки — Опыт работы с несколькими проектами — Знакомство с backend- и frontend-разработкой — Опыт создания Telegram-ботов и автоматизации процессов — Опыт работы с AI-агентами — Знакомство с affiliate-маркетингом, медиабаингом, финтехом или трейдингом Условия: — Зарплата — $1700 — График 5/2 — С 10:00 до 18:00 — полноценный рабочий день — С 18:00 до 20:00 — быть на связи — Полностью удалённая работа

Location: Berlin, Germany About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role Nebius Token Factory is looking for scientists who can turn frontier LLM and VLM inference bottlenecks into research questions, conduct rigorous experiments, and translate their findings into production capabilities. You will own focused research and optimization projects, write strong code, and produce prototypes, publications, and technical artifacts that engineers can build on. Your research will span quantization, distillation, speculative decoding, KV-cache optimization, and model/runtime co-optimization, alongside inference engines and distributed inference architectures. You will investigate how these approaches interact, evaluate their trade-offs across model quality, latency, throughput, memory footprint, and cost per token, and work with engineering teams to bring promising results into production. Your responsibilities Lead research projects in efficient LLM and VLM inference, from hypotheses and experiments through ablations, prototypes, and production handoff. Develop and evaluate methods spanning quantization, quantization-aware training (QAT), distillation, speculative decoding, KV-cache reuse, KV-cache compression, long-context inference, MoE routing, and model/runtime co-optimization. Build prototypes using PyTorch, Triton, CUDA-adjacent tooling, or inference-serving frameworks, and collaborate with MLEs and platform engineers to turn them into production components. Research LLM request routing and scheduling strategies, including cache-aware load balancing and prefill–decode disaggregation (PDD). Evaluate how queueing, KV-cache transfer, worker placement, and prefill/decode capacity allocation affect latency, throughput, and serving cost. Investigate multi-node inference for dense and mixture-of-experts models, including wide expert parallelism (WideEP). Study expert placement, load imbalance, and computation/communication trade-offs, and use the findings to guide model and system architecture choices. Develop rigorous evaluations covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token. Compare serving architectures under consistent workload conditions and GPU budgets. Work with MLE, GPU kernel, backend infrastructure, product, and customer teams to select research priorities with measurable production impact. Share results through internal reports, technical blogs, papers, and open-source artifacts, and mentor engineers and scientists on experimental design, scientific rigor, and model/system trade-offs. Must-haves A PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied mathematics, or a closely related discipline. A strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas. Strong Python and PyTorch implementation skills, with the ability to turn ideas into experiments and working prototypes. Deep knowledge of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving trade-offs. Strong experimental design skills covering ablations, baselines, metrics, statistical reasoning, and failure analysis. Excellent written and verbal communication. Nice-to-haves First-author publications at venues such as NeurIPS, ICML, ICLR, MLSys, ACL, EMNLP, ASPLOS, OSDI, SOSP, ISCA, or HPCA. Experience deploying ML models or inference optimizations in production. Experience with vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, FlashAttention, FlashInfer, Triton, CUDA, or PyTorch internals. Experience applying post-training, SFT, DPO, RLHF, RLAIF, preference optimization, or synthetic data generation to inference quality or efficiency. Open-source research artifacts, widely used benchmarks, technical blogs, or invited talks demonstrating contributions to efficient AI systems. Research or implementation experience in one or more areas of distributed inference system architecture, such as LLM request routing, PDD, multi-node serving, or MoE expert parallelism, including WideEP. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Location: Zurich, Switzerland About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role Nebius Token Factory is looking for scientists who can turn frontier LLM and VLM inference bottlenecks into research questions, conduct rigorous experiments, and translate their findings into production capabilities. You will own focused research and optimization projects, write strong code, and produce prototypes, publications, and technical artifacts that engineers can build on. Your research will span quantization, distillation, speculative decoding, KV-cache optimization, and model/runtime co-optimization, alongside inference engines and distributed inference architectures. You will investigate how these approaches interact, evaluate their trade-offs across model quality, latency, throughput, memory footprint, and cost per token, and work with engineering teams to bring promising results into production. Your responsibilities Lead research projects in efficient LLM and VLM inference, from hypotheses and experiments through ablations, prototypes, and production handoff. Develop and evaluate methods spanning quantization, quantization-aware training (QAT), distillation, speculative decoding, KV-cache reuse, KV-cache compression, long-context inference, MoE routing, and model/runtime co-optimization. Build prototypes using PyTorch, Triton, CUDA-adjacent tooling, or inference-serving frameworks, and collaborate with MLEs and platform engineers to turn them into production components. Research LLM request routing and scheduling strategies, including cache-aware load balancing and prefill–decode disaggregation (PDD). Evaluate how queueing, KV-cache transfer, worker placement, and prefill/decode capacity allocation affect latency, throughput, and serving cost. Investigate multi-node inference for dense and mixture-of-experts models, including wide expert parallelism (WideEP). Study expert placement, load imbalance, and computation/communication trade-offs, and use the findings to guide model and system architecture choices. Develop rigorous evaluations covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token. Compare serving architectures under consistent workload conditions and GPU budgets. Work with MLE, GPU kernel, backend infrastructure, product, and customer teams to select research priorities with measurable production impact. Share results through internal reports, technical blogs, papers, and open-source artifacts, and mentor engineers and scientists on experimental design, scientific rigor, and model/system trade-offs. Must-haves A PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied mathematics, or a closely related discipline. A strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas. Strong Python and PyTorch implementation skills, with the ability to turn ideas into experiments and working prototypes. Deep knowledge of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving trade-offs. Strong experimental design skills covering ablations, baselines, metrics, statistical reasoning, and failure analysis. Excellent written and verbal communication. Nice-to-haves First-author publications at venues such as NeurIPS, ICML, ICLR, MLSys, ACL, EMNLP, ASPLOS, OSDI, SOSP, ISCA, or HPCA. Experience deploying ML models or inference optimizations in production. Experience with vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, FlashAttention, FlashInfer, Triton, CUDA, or PyTorch internals. Experience applying post-training, SFT, DPO, RLHF, RLAIF, preference optimization, or synthetic data generation to inference quality or efficiency. Open-source research artifacts, widely used benchmarks, technical blogs, or invited talks demonstrating contributions to efficient AI systems. Research or implementation experience in one or more areas of distributed inference system architecture, such as LLM request routing, PDD, multi-node serving, or MoE expert parallelism, including WideEP. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Location: London, United Kingdom About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. The role Nebius Token Factory is looking for scientists who can turn frontier LLM and VLM inference bottlenecks into research questions, conduct rigorous experiments, and translate their findings into production capabilities. You will own focused research and optimization projects, write strong code, and produce prototypes, publications, and technical artifacts that engineers can build on. Your research will span quantization, distillation, speculative decoding, KV-cache optimization, and model/runtime co-optimization, alongside inference engines and distributed inference architectures. You will investigate how these approaches interact, evaluate their trade-offs across model quality, latency, throughput, memory footprint, and cost per token, and work with engineering teams to bring promising results into production. Your responsibilities Lead research projects in efficient LLM and VLM inference, from hypotheses and experiments through ablations, prototypes, and production handoff. Develop and evaluate methods spanning quantization, quantization-aware training (QAT), distillation, speculative decoding, KV-cache reuse, KV-cache compression, long-context inference, MoE routing, and model/runtime co-optimization. Build prototypes using PyTorch, Triton, CUDA-adjacent tooling, or inference-serving frameworks, and collaborate with MLEs and platform engineers to turn them into production components. Research LLM request routing and scheduling strategies, including cache-aware load balancing and prefill–decode disaggregation (PDD). Evaluate how queueing, KV-cache transfer, worker placement, and prefill/decode capacity allocation affect latency, throughput, and serving cost. Investigate multi-node inference for dense and mixture-of-experts models, including wide expert parallelism (WideEP). Study expert placement, load imbalance, and computation/communication trade-offs, and use the findings to guide model and system architecture choices. Develop rigorous evaluations covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token. Compare serving architectures under consistent workload conditions and GPU budgets. Work with MLE, GPU kernel, backend infrastructure, product, and customer teams to select research priorities with measurable production impact. Share results through internal reports, technical blogs, papers, and open-source artifacts, and mentor engineers and scientists on experimental design, scientific rigor, and model/system trade-offs. Must-haves A PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied mathematics, or a closely related discipline. A strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas. Strong Python and PyTorch implementation skills, with the ability to turn ideas into experiments and working prototypes. Deep knowledge of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving trade-offs. Strong experimental design skills covering ablations, baselines, metrics, statistical reasoning, and failure analysis. Excellent written and verbal communication. Nice-to-haves First-author publications at venues such as NeurIPS, ICML, ICLR, MLSys, ACL, EMNLP, ASPLOS, OSDI, SOSP, ISCA, or HPCA. Experience deploying ML models or inference optimizations in production. Experience with vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, FlashAttention, FlashInfer, Triton, CUDA, or PyTorch internals. Experience applying post-training, SFT, DPO, RLHF, RLAIF, preference optimization, or synthetic data generation to inference quality or efficiency. Open-source research artifacts, widely used benchmarks, technical blogs, or invited talks demonstrating contributions to efficient AI systems. Research or implementation experience in one or more areas of distributed inference system architecture, such as LLM request routing, PDD, multi-node serving, or MoE expert parallelism, including WideEP. Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Задачи: — Тестирование процесса декомпозиции монолитного приложения и переноса бизнес-функций в микросервисы; — Проверка сохранения поведения системы, API-контрактов, интеграций, миграции и обработки данных; — Проведение функционального, интеграционного, регрессионного и сквозного тестирования; — Выявление расхождений между монолитом и микросервисной реализацией; — Ведение тест-кейсов, чек-листов, дефектов, результатов тестов, фиксация тестовых данных и ежедневная отчетность. Требования: — Опыт работы от 3 лет в тестировании backend-сервисов, API и интеграций между системами; — API-тестирование: от 3 лет; — Тестирование микросервисов: от 3 лет; — SQL/PostgreSQL: от 2 лет; — Асинхронные процессы и очереди: от 2 лет; — Docker/Kubernetes: от 1 года; — Jira/Confluence/GitLab: от 1 года; — Не менее трех коммерческих Enterprise-проектов; — Опыт тестирования микросервисной архитектуры и интеграций с legacy-системами; — Владение Python, pytest или аналогичным фреймворком, REST API, Postman/Insomnia, Swagger/OpenAPI, PostgreSQL, JSON/JSONB, GitLab, Docker, Kubernetes, S3-хранилища, инструментами мониторинга и анализа логов. Будет преимуществом: — Опыт написания unit, integration и contract тестов; — Навыки работы с RestAssured/Newman, Kafka или RabbitMQ, Redis, MinIO, Grafana, Prometheus, OpenTelemetry, Allure/TestRail/Zephyr, Charles/Fiddler, PlantUML/C4. Условия: — Проектная занятость, долгосрочный проект (фултайм); — ГПХ или ИП; — Удаленная работа по РФ (строго Москва, Саранск); — Почасовая оплата до 1500₽/час гросс.

Кто мы и что строим: Flowwow — международный маркетплейс подарков. Под ним работает Data Platform, построенная с нуля: lakehouse на Apache Iceberg + Trino, Spark для тяжёлых вычислений, CDC-пайплайны на Kafka/Debezium, всё в Kubernetes. Собственный код: Data Vault — моделирование хранилища как код, обвязка Airflow с собственными операторами и хуками, lineage и инструменты наблюдаемости, AI-слой с агентами на LangGraph + MCP для расследования инцидентов и анализа качества данных. Почему эта роль интересная: Позиция Senior Data Engineer с уклоном в платформенную разработку. Проектирование инструментов для ускорения написания пайплайнов, работа с lakehouse-стеком на глубоком уровне: оптимизация Trino-запросов, maintenance Iceberg-таблиц, эволюция схем, REST-каталог. AI-native команда, ежедневное использование Claude Code и аналогичных инструментов, построение harness-систем вокруг LLM. Задачи: — Развивать ядро платформы: Data Vault, обвязка Airflow, lineage-инструменты, проектировать API и нести за них ответственность. — Писать библиотеки и фреймворки на Python: клиенты к источникам, инструменты тестирования пайплайнов, обёртки над инфраструктурой. — Работать с lakehouse: Iceberg maintenance, оптимизация layout таблиц, тюнинг Trino, Spark-джобы. — Строить интеграции с источниками: OLTP-базы, REST/gRPC API, Kafka, файловые хранилища, SaaS-системы, проектировать унифицированные интерфейсы. — Внедрять AI в инженерные процессы: агенты для расследования инцидентов, ассистенты для DAG'ов и code review. — Развивать агентную инфраструктуру (LangGraph, MCP). — Участвовать в архитектурных решениях: выбор технологий, дизайн интерфейсов, best practices. — Делать code review и менторить коллег. Требования: — Опыт от 5 лет в data engineering или backend-разработке с сильным дата-уклоном, опыт эксплуатации production-систем. — Python — главное требование. — Уровень senior: ООП, проектирование API, типизация, тесты, переиспользуемые компоненты. — Практический опыт с lakehouse-стеком: Apache Iceberg (или Delta/Hudi), понимание отличий lakehouse от классического DWH. — Опыт с Trino или другим MPP-движком: архитектура, оптимизация, коннекторы. — Глубокие знания Apache Airflow: внутреннее устройство, собственные операторы и хуки. — Опыт с Apache Spark: PySpark, Spark SQL. — Продвинутый SQL: оконные функции, CTE, оптимизация, работа с большими объёмами. — Уверенное владение Git, CI/CD. — Будет плюсом: опыт разработки внутренних платформенных инструментов, AI-native подход, Kubernetes, Helm, CDC (Debezium, Kafka Connect), знание форматов хранения Parquet, опыт с системами контроля доступа к данным (OPA, row-level security, маскирование). Условия: — Полная занятость. — Трудовой договор. — График 5/2. — Удалённый формат работы. — Уровень дохода обсуждается на этапах найма. Дополнительно: — ДМС со стоматологией или компенсация на медобслуживание, сервис психологической поддержки. — Компенсация расходов на спорт и спортивное оборудование. — Компенсация стоимости техники для работы. — Оплата обучения, конференций, митапов. — Корпоративная программа лояльности с персональными скидками и предложениями от партнёров. Наши ценности: — Забота и любовь к продукту. — Развитие и рост. — Культура взаимопомощи. — Открытость к изменениям. — Благотворительность и социальная ответственность.

Location: Paris; Amsterdam; Munich; London; Berlin Workplace: Remote 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 TEAM Mistral is building a sovereign AI cloud for Europe. Our customers in finance, healthcare, and the public sector run frontier models on European-jurisdiction infrastructure, and the Cloud Platform team builds the systems that make that possible — compute orchestration, Kubernetes, bare metal, storage, networking, and the datacenter fleet that runs it all. We run it all on our own metal, our own network, our own datacenters — which means when something breaks, the fix is ours to write. European Compute Units let enterprises commit to multi-year capacity on that infrastructure, and we are scaling toward a gigawatt of compute across Europe by 2030. You will design and ship the layer every sovereign AI deployment depends on and shape the team building it. THE ROLE This is a people-leadership role from day one. You will manage the team — hiring, coaching, performance, growth paths, honest feedback — while staying close to the code: roughly half your time in architecture decisions and the hardest technical problems the team faces, the other half leading your squad. If you are looking for a senior IC role, this is not it. You will lead the team building our serverless containers product: the way developers run AI apps, MCP servers, APIs, and managed workers on our cloud without thinking about servers. Push a container, get an endpoint, scale from zero to thousands of instances and back down — across CPU and GPU. On a hyperscaler, the serverless layer is a product someone else built. Here you own it end to end: the scheduler that decides where a container lands, the routing path a request takes, the machinery that turns a scaled-to-zero service into a warm instance before a customer notices. All of it runs on metal, network, and datacenters we own. That tension is the product: scale-to-zero economics with cold starts in milliseconds, autoscaling that absorbs a launch spike without a config change, GPUs scheduled onto containers the way other platforms schedule CPU. The customers betting on this layer are the developers building the next generation of AI software on European infrastructure. WHAT YOU WILL DO - Own the container platform end to end: image delivery, placement, request routing, scale-up, scale-to-zero — across CPU and GPU. - Make cold starts a solved problem: snapshotting, prewarming, image-layer caching, whatever it takes for scale-from-zero to feel instant. - Treat AI workloads as the normal case: GPU-backed containers, streaming responses, long-lived connections, MCP servers that cost almost nothing while idle. - Write Go alongside your team and own the architecture and the hardest implementation decisions. - Grow the engineers, not just the software — set the standard for ownership and coach people past their comfort zone. - Own people management for the team: hiring, performance, growth paths, and honest feedback. - Partner with Product Managers to turn platform decisions into a product developers want to deploy on. WHAT WE'RE LOOKING FOR - 3+ years of real people leadership. You have managed engineers — hired, coached, given honest feedback, and grown them. Hands-on contribution matters here, but this role is judged on the team you build and lead, not just the code you write. - You have shipped production systems in Go. Not dabbled. Shipped. - You have built container or workload platforms — orchestration, scheduling, isolation, request routing — not just deployed onto someone else's serverless platform. - Deep Linux: namespaces, cgroups, image layers, page cache — comfortable well below the application layer. - Multi-tenancy instincts: density, isolation, and fairness when hundreds of customers share one fleet. - Significant experience building complex backend or infrastructure systems. - You have partnered with product, design, and business teams to ship software that reached real users. - A low-ego, team-first mindset. We care less about your exact domain and more about whether you are the kind of technical force other engineers want to work alongside. 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: 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 looking for Mobile Engineers to join our Compliance engineering team. In this role, you will build secure, regulation-compliant, and high-trust mobile solutions that power global onboarding, identity verification, risk control, and regulatory compliance systems. We are open to candidates across a wide spectrum of experience. Whether you are an engineer early in your career eager to master mobile security and regulatory technology, or a seasoned practitioner who can architect resilient systems and drive technical direction across global markets, you will find high impact and technical depth here. You should be self-driven, hungry to grow, and motivated by solving critical real-world engineering challenges. What You'll Be Doing Build, maintain, and scale native iOS features and user flows using Swift and modern iOS frameworks. Collaborate with Product, Design, and Backend Engineering to translate functional requirements into smooth, responsive mobile experiences. Integrate third-party SDKs, native frameworks, and hardware capabilities (such as camera APIs, biometrics, or local security features) with a focus on UX and performance. Contribute to mobile client architecture, keeping the codebase modular, testable, and maintainable. Participate in code reviews, testing, and continuous optimization to maintain high iOS engineering standards. Leverage modern development tools and AI coding assistants to streamline your daily workflow and accelerate delivery. Diagnose and fix edge cases, performance bottlenecks, and production bugs to ensure a rock-solid iOS app experience. What We Look For In You Hands-on native iOS engineering experience with Swift, SwiftUI, and UIKit . Solid grasp of software engineering principles, memory management, multithreading, and mobile system design. Experience integrating RESTful APIs into production iOS applications. Commitment to writing clean, well-tested, and maintainable code. Strong problem-solving mindset with a desire to build high-quality software. Openness to using modern developer tools and AI coding assistants (e.g., Claude Code, Codex) in your everyday coding workflow. Nice-To-Haves Exposure to mobile CI/CD pipelines (e.g., Fastlane, GitHub Actions, Xcode Cloud). Experience working with modular iOS architectures or building internal shared SDKs/frameworks. Familiarity with unit testing frameworks (XCTest) and UI testing on iOS. Experience with feature flagging, analytics, or A/B experimentation frameworks. 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! #LI-ML1 #LI-ONSITE 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 .