Вакансии

Location: Remote, Global ABOUT SUPABASE Supabase is the Postgres development platform, built by developers for developers. We provide a complete backend solution including Database, Auth, Storage, Edge Functions, Realtime, and Vector Search. All services are deeply integrated and designed for growth. ABOUT THE ROLE We're looking for a Release Engineer (SRE) to join our Release Engineering team (part of EngOps) — a production-operations expert who brings an SRE mindset to how Supabase ships and runs, making deploys safe, observable, and recoverable at scale. Release Engineering's scope has grown well beyond build-and-ship: we increasingly own the operational reliability of the systems that deploy and run Supabase. In this role you'll treat our deployment pipelines, pre-production signal, and the control plane itself as production systems — with SLOs, error budgets, and on-call ownership — and you'll be the person teams lean on when reliability is on the line. This is not a "gatekeeper" role. You'll make the reliable path the easy path: standardising how we deploy, instrumenting what we ship, and ensuring that when something breaks, we detect it quickly and recover quickly. WHAT YOU'LL BE RESPONSIBLE FOR In this role, you'll: - Own the reliability of Supabase's deployment and release systems, and the control plane they run on, against clear SLOs and error budgets - Turn pre-production into a trustworthy signal — standardizing and instrumenting today's fragmented, ad-hoc deployment workflows - Drive disaster-recovery readiness, including making environments reproducibly deployable from scratch (untangling undocumented secrets, unclear configuration ownership, and circular service dependencies) - Build and operate health and SLO monitoring for critical user flows, using synthetic testing to catch regressions before customers do - Reduce mean-time-to-detect and mean-time-to-recover for deploy-related incidents — which account for a large share of our incident load - Participate in on-call, lead blameless postmortems, and turn findings into runbooks, alerting, and automation that remove toil - Improve deployment observability and auditability — a clear record of what shipped where, when, and by whom - Document operational procedures — break-glass paths, access models, and runbooks — so reliability knowledge isn't tribal RELIABILITY & OPERATIONS - Define and track SLAs, SLOs, error budgets, and DORA delivery metrics — with meaningful alerting over noise - Ensure deployments fail fast and safely when health checks degrade - Harden access and break-glass workflows (e.g. scoped self-service) so the right people can act in an incident without unsafe workarounds - Partner with product engineering and platform teams to align release practices with reliability and availability targets YOU MIGHT BE A GOOD FIT IF YOU - Have 5+ years in SRE, production operations, platform engineering, or release engineering - Have operated production systems at scale and carried on-call for them - Are fluent in SLAs, SLOs, error budgets, DORA metrics, and operational KPIs — and the observability tooling behind them (Prometheus, Grafana, Alertmanager, or similar) - Have led incident response with tooling like incident.io http://incident.io (or PagerDuty / Opsgenie), run blameless postmortems, and driven down MTTD/MTTR - Operate confidently on AWS (multiple accounts, IAM, VPC) in production - Are comfortable with infrastructure-as-code (Pulumi, Terraform) and Kubernetes - Script and automate to eliminate toil rather than absorb it - Communicate clearly with both infrastructure specialists and product engineers - Thrive in async, globally distributed teams - Are comfortable navigating ambiguity and iterating toward better systems over time WHAT WE OFFER - Fully Remote We hire globally. We believe you can do your best work from anywhere. There are no Supabase offices, but we provide a WeWork membership or co-working allowance you can use anywhere in the world. - ESOP Every team member receives ESOP (equity ownership) in the company. We want everyone to share in the upside of what we’re building together. - Tech Allowance Use this budget to set up your ideal work environment—laptop, monitor, headphones, or whatever helps you do your best work. - Health Benefits Supabase covers 100% of health insurance for employees and 80% for dependents, wherever you are. Your wellbeing and your family’s health are important to us. - Annual Off-Sites Once a year, the entire company gathers in a new city for a week of connection, collaboration, and fun. It’s a highlight of our year. - Flexible Work We operate asynchronously and trust you to manage your own time. You know what needs to be done and when. - Professional Development Every team member receives an annual education allowance to spend on learning—courses, books, conferences, or anything that supports your growth. ABOUT THE TEAM Supabase was born-remote and open-source-first. We believe our globally distributed team is our secret weapon in building tools developers love. - ~400 team members - 60+ countries - 20+ languages spoken - Over $1B raised (including our $500M Series F) - 540,000+ community members We move fast, build in public, and use what we ship. If it’s in your project, we probably use it in ours too. We believe deeply in the open-source ecosystem and strive to support—not replace—existing tools and communities.

Location: Москва HR-платформа Сбера "Пульс" ищет Java/Kotlin лида разработки. Обязанности * Разработка и поддержка backend-сервисов на Kotlin/Java в рамках текущего стека (Spring Boot 3, Exposed, PostgreSQL, Kafka, K8s) * End-to-end владение бэкендом: от обсуждения требований с системным аналитиком до выката в продакшен и последующей эксплуатации * Проектирование схем БД, миграций, доменных моделей и контрактов API * Реализация интеграций через Kafka и REST с внешними и внутренними системами с учётом отказоустойчивости и идемпотентности * Настройка деплоя и эксплуатации сервисов в Kubernetes (манифесты/Helm, конфиги, секреты), участие в CI/CD-пайплайне * Покрытие кода юнит- и интеграционными тестами (Testcontainers), совместная работа с тестировщиком над сценариями и приёмкой * Совместная работа с системным аналитиком: уточнение требований, валидация постановок на реализуемость, влияние на декомпозицию * Контроль качества и техдолга на бэкенде: code style, ревью собственных решений, рефакторинг, документация * Участие в инцидент-менеджменте: разбор проблем на проде, дежурство в разумных пределах, постмортемы * Технологические решения по стеку и архитектуре бэкенда — самостоятельно, согласовывая ключевые с тимлидом/архитектором * Соблюдение требований домена: безопасность данных, аудит операций, корректность денежных расчётов Требования * 5+ лет коммерческой разработки бэкенда на JVM, из них значимый опыт на Kotlin (желательно Kotlin 2) и/или современной Java (17+, опыт с Java 21 — плюс) * Глубокое знание Spring Boot 3, включая Spring Data, Spring Security, конфигурацию, тестирование (Spring Test, Testcontainers) * Уверенная работа с PostgreSQL: проектирование схем, индексы, оптимизация запросов, объяснение планов, миграции (Flyway/Liquibase), понимание уровней изоляции и транзакций * Опыт с Kotlin Exposed (или готовность быстро перейти с JPA/Hibernate/jOOQ) * Практический опыт с Apache Kafka: продюсеры/консюмеры, партиционирование, гарантии доставки, идемпотентность, обработка ошибок и DLQ * Опыт эксплуатации сервисов в Kubernetes: написание манифестов/Helm-чартов, конфигурация ресурсов, health/readiness probes, понимание сетевой модели, секретов, графика выкатки * Уверенный CI/CD: GitLab CI / GitHub Actions / Jenkins, сборка артефактов, контейнеризация (Docker), автоматические тесты в пайплайне * Понимание архитектурных паттернов: чистая/гексагональная архитектура, DDD-подход к моделированию домена, идемпотентность, транзакционные паттерны (outbox, saga), eventual consistency * Опыт проектирования и сопровождения REST API (OpenAPI/Swagger), понимание контрактного подхода * Самостоятельность и продуктовое мышление: умение декомпозировать задачу из бизнес-постановки в технические шаги, принимать архитектурные решения и нести за них ответственность * Внимание к надёжности: метрики, логирование, трейсинг (Prometheus/Grafana/OpenTelemetry или аналоги), умение разбирать инциденты в проде * Опыт работы в FinTech или другом домене с высокими требованиями к корректности денежных операций, аудиту и безопасности — сильное преимущество * Английский на уровне чтения технической документации Будет плюсом *   Опыт работы единственным бэкендером в команде или в условиях высокой автономии * Знание coroutines в Kotlin, опыт реактивного программирования (WebFlux, Reactor) * Опыт с распределёнными транзакциями, saga, transactional outbox в продакшене * Опыт с инфраструктурой как код (Terraform/Helmfile/ArgoCD) Условия * Инновационные, амбициозные проекты и задачи, которые развивают: всегда есть возможность прокачать свои навыки в работе и профессионально расти; * Среда для обмена знаниями – высокая экспертиза внутри команды; * Сплоченная команда, работающая над общими задачами и умеющая хорошо отдыхать; * Нашу культуру создают сами сотрудники – мы их слышим и помогаем создавать и поддерживать корпоративные комьюнити по интересам * Стабильная заработная плата и годовой бонус; * Гибридный формат работы. Современный IT-офис вблизи Москва-Сити в пяти минутах от метро "Кутузовская", с фитнес залом; * Более 400 образовательных программ СберУниверситета для профессионального и карьерного развития;  * 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 использования трёх близких; * Вознаграждение за рекомендацию друзей в команду Сбера.

Location: Singapore About the Company Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team. The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks within a single intelligent assistant. Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-step reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive. About the Role Our client is seeking a Technical Lead, Machine Learning to lead the execution of its AI platform by translating research into scalable, production-ready machine learning systems. This role sits at the intersection of research, infrastructure, and product, with responsibility for ensuring models are trainable, deployable, observable, and optimized for real-world performance. Working closely with research, engineering, and product teams, this position will drive the development of robust ML infrastructure while balancing performance, reliability, latency, and cost. Key Responsibilities Lead the end-to-end execution of machine learning systems, including data pipelines, training workflows, evaluation frameworks, inference architecture, and production deployment. Fine-tune and optimize models using modern techniques such as LoRA, QLoRA, Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and model distillation . Design, build, and operate scalable inference systems with a focus on latency, cost efficiency, and reliability. Develop and maintain data pipelines for both synthetic and real-world training datasets. Build evaluation frameworks to measure model performance, robustness, safety, and bias in collaboration with research teams. Optimize production deployments through GPU utilization, memory efficiency, inference optimization, and scaling strategies. Partner closely with application engineering teams to integrate machine learning systems into backend, desktop, and mobile products. Continuously improve production systems through rapid iteration, monitoring, and data-driven optimization. Requirements Proven experience building and deploying production-grade machine learning systems used by real users. Strong expertise working with large language models and understanding model behavior, limitations, and failure modes. Experience developing scalable ML infrastructure, training pipelines, and inference systems. Strong software engineering skills with the ability to write maintainable, production-quality code. Experience balancing real-world production constraints, including latency, reliability, scalability, cost, and safety. Strong ownership mindset with the ability to independently drive technical initiatives from design through deployment. Excellent communication and collaboration skills, with experience working in cross-functional, high-performing engineering teams. Preferred Technical Skills Experience with the following technologies is preferred: Python PyTorch and/or JAX GPU-based model training and inference systems

Location: Singapore About the Company Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team. The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks within a single intelligent assistant. Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-step reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive. About the Role Our client is seeking a Principal Machine Learning Engineer to build and deploy production-grade machine learning systems that power its AI platform. This role focuses on translating research into scalable solutions by developing robust training pipelines, inference systems, evaluation frameworks, and deployment infrastructure. Working closely with research and application engineering teams, this position will play a key role in delivering reliable, high-performance ML systems that operate effectively under real-world production constraints. Key Responsibilities Build and own end-to-end machine learning pipelines covering data processing, model training, evaluation, inference, and deployment. Fine-tune and adapt models using modern techniques such as LoRA, QLoRA, Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and model distillation. Design and operate scalable inference systems while balancing latency, cost, and reliability. Develop and maintain data pipelines for both synthetic and real-world training datasets. Build evaluation frameworks to assess model performance, robustness, safety, and bias in collaboration with research teams. Optimize production deployments through GPU optimization, memory efficiency, latency reduction, and scaling strategies. Collaborate with application engineering teams to integrate machine learning systems into backend, mobile, and desktop applications. Continuously improve ML systems through rapid iteration and real-world performance monitoring while balancing production constraints such as latency, cost, reliability, and safety. Requirements Strong background in deep learning and transformer-based architectures. Hands-on experience training, fine-tuning, or deploying large-scale machine learning models in production. Proficiency with modern machine learning frameworks such as PyTorch or JAX . Experience with distributed training and inference frameworks, including technologies such as DeepSpeed, FSDP, Megatron, ZeRO, or Ray . Strong software engineering skills with experience building robust, maintainable, production-grade systems. Experience optimizing GPU workloads, including memory efficiency, quantization, and mixed precision. Ability to independently own end-to-end machine learning systems in fast-moving environments. Strong problem-solving skills with a focus on rapid iteration and continuous improvement. Preferred Qualifications Experience with one or more of the following is preferred: LLM inference frameworks such as vLLM, TensorRT-LLM, or FasterTransformer Open-source contributions to machine learning or systems libraries Scientific computing, compiler technologies, or GPU kernel development Reinforcement Learning from Human Feedback (RLHF) pipelines, including PPO, DPO, or ORPO Training or deploying multimodal or diffusion models Large-scale data processing frameworks such as Apache Arrow, Spark, or Ray

Location: Singapore About the Company Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team. The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks within a single intelligent assistant. Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-step reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive. About the Role Our client is seeking an iOS Software Engineer to build and enhance its AI-native mobile experience. This role is responsible for developing production-grade iOS applications where AI interactions are central to the user experience, ensuring performance, reliability, and user trust across all mobile interactions. Working closely with backend and machine learning teams, this position will focus on delivering scalable iOS applications that integrate advanced AI capabilities while maintaining a seamless and intuitive user experience. Key Responsibilities Build and maintain production iOS applications using Swift and SwiftUI . Integrate AI-powered features through backend APIs. Design user experiences for AI interactions, including loading states, streaming responses, retries, and fallback mechanisms. Optimize application performance, memory usage, and battery efficiency. Implement user feedback and analytics mechanisms to support AI evaluation and continuous product improvement. Collaborate closely with backend and machine learning engineers on API design and system behavior. Maintain high standards of application quality, stability, and reliability for App Store releases. Requirements Minimum 3 years of iOS development experience using Swift. Hands-on experience integrating AI-powered features into mobile applications. Strong understanding of async/await , concurrency, and background task management. Solid experience optimizing iOS application performance, memory usage, and responsiveness. Experience developing, releasing, and maintaining production iOS applications. Familiarity with designing applications that operate reliably under latency, partial failures, and non-deterministic AI behavior. Exposure to CoreML or lightweight on-device machine learning is an advantage. Familiarity with feature flags or remote configuration systems is preferred. Strong understanding of user experience design for AI-powered applications, with an emphasis on usability and user trust. Preferred Technical Skills Experience with the following technologies is preferred: Swift SwiftUI Swift Package Manager (SPM) SQL and NoSQL databases TensorFlow Lite (on-device inference)

Location: Singapore About the Company Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team. The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks within a single intelligent assistant. Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-step reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive. About the Role Our client is looking for a Full Stack Engineer – AI Systems to build AI-native product experiences that transform advanced AI capabilities into intuitive, production-ready workflows. This role combines frontend, backend, and AI engineering to develop intelligent features that are reliable, scalable, and user-centric. Working closely with product, backend, and machine learning teams, this position will focus on designing end-to-end AI workflows, integrating large language models (LLMs), and delivering seamless user experiences across web applications. Key Responsibilities Build end-to-end product features across frontend, backend, and AI integrations. Design and develop AI agent workflows that support planning, tool usage, failure handling, and recovery across multiple steps. Integrate LLMs, memory systems, and external tools into scalable, production-grade applications. Design responsive AI interactions with streaming capabilities, partial results, and low-latency performance. Improve system reliability, observability, and fallback mechanisms for AI-powered features. Collaborate closely with product, backend, and machine learning teams to deliver features from concept to production. Continuously iterate and improve product functionality based on user feedback, production insights, and real-world usage. Requirements Strong experience in full stack software engineering across frontend and backend development. Solid understanding of system design, scalable architecture, and API development. Experience building AI-powered applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), or similar technologies. Ability to work effectively in fast-paced environments with evolving requirements. Strong ownership mindset with experience delivering features from ideation through production deployment. Excellent problem-solving skills and the ability to make pragmatic engineering decisions in ambiguous situations. Preferred Technical Skills Experience with some or all of the following technologies is preferred: Next.js Python Node.js PyTorch OpenAI, Anthropic, or open-source Large Language Models (LLMs) SQL and NoSQL databases Kubernetes Docker

Location: Singapore About the Company Our client is a stealth AI startup backed by one of Southeast Asia's leading technology companies and is currently building its global founding team. The company is developing an AI-native communication platform designed to simplify everyday tasks by integrating AI directly into conversations. Instead of switching between multiple applications, users can plan, organize, compare, research, and complete tasks within a single intelligent assistant. Serving a market of billions of users still relying on traditional productivity tools, the platform focuses on delivering reliable AI workflows, persistent context, multi-step reasoning, and seamless task execution. The mission is to create an AI assistant that significantly improves productivity while making everyday work simpler and more intuitive. About the Role Our client is seeking an Android Software Engineer to build and enhance its AI-native mobile experience. This role is responsible for developing production-grade Android applications where AI is at the core of the user experience, ensuring performance, reliability, and usability across all mobile interactions. Working closely with backend and machine learning teams, this position will focus on delivering scalable Android applications that integrate advanced AI capabilities while maintaining a seamless user experience. Key Responsibilities Build and maintain production Android applications using Kotlin . Integrate AI-powered features such as chat, vision, voice, and recommendation systems through backend APIs. Design intuitive user experiences for AI interactions, including streaming responses, retries, and partial results. Optimize application performance, memory usage, and responsiveness for AI-intensive workflows. Implement analytics, logging, and user feedback mechanisms to support continuous AI evaluation and product improvement. Collaborate closely with backend and machine learning engineers to define API integrations and system behavior. Ensure application stability, security, and scalability within production environments. Requirements Minimum 3 years of Android application development experience using Kotlin. Hands-on experience integrating AI-powered features, such as Large Language Models (LLMs), computer vision, or speech APIs. Strong understanding of asynchronous programming using Coroutines and Flow . Experience working with RESTful or gRPC APIs and structured data formats. Strong debugging, troubleshooting, and performance profiling skills. Experience developing applications that operate reliably under conditions involving latency, partial failures, and non-deterministic behavior. Familiarity with ML Kit or lightweight on-device inference is an advantage. Experience publishing and maintaining production applications on the Google Play Store . Preferred Technical Skills Experience with the following technologies is preferred: Kotlin / Java SQL and NoSQL databases TensorFlow Lite (on-device inference)

Компания: Продуктовая IT-компания с офисом в Бостоне, США. Сервис помогает быстрее находить, оценивать и продвигать коммерческую недвижимость с помощью AI и глубокой аналитики рынка. Решение для брокеров, инвесторов и собственников, которым нужны актуальные листинги, точные инсайты и более быстрые решения по сделкам. Почему это интересно: — Продукт с реальной ценностью: Big Data + AI — Сильная команда инженеров и продуктовых специалистов — Минимум бюрократии, быстрые решения — Влияние на архитектуру и финальный продукт — Удаленная работа — Бонусы и stock options — Возможность релокации / стажировки в США — Время и ресурсы на рефакторинг и улучшения — Внутренние QA — разработчики не остаются один на один с качеством Задачи: — Разработка и развитие backend-компонентов продукта — Архитектурное проектирование и развитие серверной части системы — Руководство командой backend-разработки — Проведение code review, контроль качества кода и соблюдения инженерных стандартов — Участие в оценке задач, планировании спринтов и управлении техническим долгом — Проработка и внедрение решений по повышению производительности, стабильности и масштабируемости системы — Участие в проектировании и реализации новых функциональных модулей — Подготовка и актуализация технической документации — Взаимодействие с product-командой, дизайнерами и смежными техническими ролями — Наставничество разработчиков, участие в найме и онбординге — Внедрение и использование AI-агентов в продуктовых и внутренних процессах — Оценка, внедрение и масштабирование AI-решений — Обеспечение наблюдаемости, надёжности и управляемости AI-сервисов Требования: — Опыт в коммерческой разработке от 7 лет — Практическое использование AI агентов в процессах разработки от 1,5 лет — Опыт работы на каждом месте не менее 2,5 лет — Опыт управления командой от 3 лет — Английский не ниже В1 — Высшее техническое образование строго из списка ВУЗов: МФТИ, МГУ, НИУ ВШЭ, НИЯУ МИФИ, МГТУ имени Н. Э. Баумана, РУДН, МАИ, «МИСИС», ИТМО, СПбГУ, «ЛЭТИ», СПбПУ Петра Великого, УрФУ, Иннополис, ЮФУ, ТПУ, КФУ, НГУ, ТГУ, СФУ, Белорусский государственный университет, Белорусский национальный технический университет, Белорусский государственный технологический университет, БГУИР, КНУ им. Тараса Шевченко, КПИ им. Игоря Сикорского, ХПИ (Харьковский политех). Технологии: — PHP 8.x — MySQL / PostgreSQL — Redis — Elasticsearch — SQL — Linux — Git Будет преимуществом опыт работы с: — RabbitMQ — Go — Python — FastAPI — SQLAlchemy — Celery Условия: — График работы 5/2, оплачиваемый отпуск — Дистанционный формат работы из любой точки мира — Отсутствие бюрократии и микроменеджмента - ориентируются на результат

Задачи: — Разрабатывать новые mini apps и продуктовые модули для trading web-платформы с экосистемой mini apps (tournaments, challenges, loyalty, achievements, education и другие engagement-механики); — Создавать frontend на React + TypeScript; — Разрабатывать backend на Python (FastAPI / Django); — Интегрировать frontend с API, торговыми и аналитическими сервисами; — Проектировать архитектуру, участвовать в code review и технической декомпозиции; — Использовать AI-инструменты (Cursor, Claude Code, Codex) для ускорения разработки с обязательной проверкой качества кода. Требования: — Коммерческий опыт с React, TypeScript и Python; — Опыт разработки на FastAPI и/или Django; — Опыт работы с PostgreSQL/MySQL, Redis и REST API; — Понимание архитектуры SPA, Docker и CI/CD; — Опыт написания тестов и проведения code review; — Умение писать безопасный, масштабируемый и поддерживаемый код. Будет плюсом: — Опыт в fintech, trading, betting, gaming или iGaming; — Опыт с WebSocket, Kafka, event-driven архитектурой; — Знание Kubernetes, OpenAPI, BFF; — Понимание OWASP и принципов безопасной разработки. Условия: — Современный стек (React, TypeScript, FastAPI, Django, PostgreSQL, Redis, Docker); — Работа над высоконагруженным продуктом с real-time механиками; — Сильная продуктовая команда и обязательное code review; — Возможность влиять на архитектуру и развитие платформы; — В первую очередь рассматриваются кандидаты, которые уже находятся на Кипре.

Location: Oakland, California, United States, AMER From Fivetran’s founding until now, our mission has remained the same: to make access to data as simple and reliable as electricity. With Fivetran, customer data arrives in their warehouses, canonical and ready to query, with no engineering or maintenance required. We’re proud that more organizations continue to leverage our technology every day to become truly data-driven. About Us Fivetran and dbt Labs are bringing together two industry-leading companies with a shared mission: helping organizations unlock the full value of their data. Together, we’re delivering the data infrastructure layer that helps organizations move, transform, and trust their data — from the moment data moves, through every transformation, to the context teams and AI systems rely on. Fivetran helps organizations automate data movement across the systems, clouds, engines, and tools they rely on. dbt Labs pioneered analytics engineering, helping teams transform data into reliable, governed insights. Together, we support thousands of organizations as they build a trusted foundation for analytics, AI, and better business decisions. As we bring our teams and technology together, we’re building on the strengths of both companies while continuing to deliver the products and experiences our customers know and trust. It’s an exciting time to join us: we’re creating a company with the scale, talent, and technology to help more organizations put their data to work with greater speed, confidence, and impact. During this transition period, you may see references to both Fivetran and dbt Labs throughout our recruiting process as we integrate our teams, systems, and career sites. About the Role If the idea of contributing to the critical and core services that drive the fundamental functioning and building blocks of data processing involved in our data pipelines, this opportunity fits you well. As an engineer on the core team, you will design,  implement, and maintain the central parts of our data pipelines needed to operate the best-in-class reliable, secure, and scalable data movement.  You will prioritize the quality, efficiency, and reliability of the responsibility areas of the team while collaborating effectively with other peers and the rest of the engineering organization. The ideal candidate exhibits exceptional technical skills, meticulous attention to detail, creativity, motivation, and a results-oriented mindset. This is a full-time hybrid position based out of our Oakland office. Our hybrid work model offers a blend of remote flexibility and in-person collaboration, including two days in the office each week to connect and build as a team. Technologies You’ll Use Java17, JUnit5 Docker and Kubernetes AWS, GCP, Azure SQL, BigQuery Bazel, BuildKite What You’ll Do Design and implement features and performance improvements to create highly scalable distributed data pipelines on the cloud Develop, maintain, and test code Conduct design and code reviews Collaborate with peers, product managers, customer support, and other stakeholders to deliver the best product with high quality, performance, and scalability. Understand and fix challenges related to performance and scalability. Participate periodically in on-call rotation Participate in the hiring process Mentor Junior developers Make Fivetran the most reliable and secure component of our customers’ data infrastructure Skills We’re Looking For 5+ years of experience in the software industry with a passion for solving complex software engineering problems. Software engineering foundation - experience designing and developing reusable libraries and experimentation platforms for backend and frontend from scratch leveraging industry best practices. Experience working with distributed systems Demonstrated ability to write well-structured and performant code Ability to lead software design to satisfy project requirements Experience in code reviews, design, troubleshooting and testing Experience programming in an object oriented / systems language such as Java, C++, C# etc Hands-on experience working with any cloud technologies(AWS, GCP, Azure) and containerization methodologies like Docker, K8s Experience interacting with continuous integration tools like Buidkite Bonus Skills​ Knowledge of observability tools like New Relic/Grafana is a plus Knowledge of AI tools and usage is a plus #LI-HYBRID #LI-EM1 The compensation range displayed on this job posting reflects the minimum and maximum target for new hire compensation for the target position and level, and may include sales incentives or target bonuses depending on the role.  Our compensation ranges are determined by role, level, and location. Our job titles may span more than one career level. Within the range, individual compensation is determined by additional factors, including job-related skills, experience, relevant education or training, business need, market demands. The compensation range is subject to change and may be modified in the future. Your recruiter can share more about the specific compensation range for your location during the hiring process. Oakland Pay Range $174,915 — $209,906.50 USD Perks and Benefits 100% employer-paid medical insurance * Generous paid time away from work, including paid vacation or flexible/unlimited vacation depending on role and country, plus paid sick time, inclusive parental leave, paid holidays, including a year-end Global Week of Rest, and volunteer days off. RSU stock grants* Professional development and training opportunities Company virtual happy hours, free food, and fun team-building activities Monthly cell phone stipend Access to an innovative mental health support platform that offers personalized care and resources in areas such as: therapy, coaching, and self-guided mindfulness exercises for all covered employees and their covered dependents. * May vary by country and worker type - please reach out to your recruiter for more information Click here to learn more about Fivetran's Benefits by Region. We’re honored to be valued at over $5.6 billion , but more importantly, we’re proud of our core values of Get Stuck In, Do the Right Thing, and One Team, One Dream . Read about us in Forbes . Fivetran brings together high-quality talent across the globe to make data access as easy and reliable as electricity for our customers. We value and recognize that our customers benefit from having innovative teams made of people from many backgrounds, experiences, and identities. Fivetran promotes diversity, equity, inclusion & belonging through attracting, recruiting, developing, and retaining a diverse workforce, not only because it is the right thing to do, but because it helps us build a world-class company to better serve our customers, our people and our communities. To learn more about Fivetran’s culture and what it’s like to be part of the team, click here and enjoy our video. To learn more about our candidate privacy policy, you can read our statement here . We are committed to ensuring that all candidates have an equal opportunity to participate in our interview process. If you require accommodations at any stage of the process due to a disability, medical condition, or any other circumstance, please don't hesitate to submit your request by filling out this form . We will work with you to provide reasonable accommodations to facilitate your participation and ensure a fair and accessible interview experience. Your request and any information provided will be kept confidential and will not impact your candidacy. We look forward to hearing from you and accommodating your needs to the best of our ability.

Location: United States (Remote); United Kingdom (Remote) Workplace: Remote About Teleport Don't wait for the future of infrastructure. Be part of it. Teleport is the AI Infrastructure Identity Company. We're solving one of the hardest problems in security: giving every human, machine, workload, and AI agent a cryptographically secured identity, improving engineering velocity while maintaining security. We make trusted computing simple. This gives you the freedom, power, and autonomy to build and innovate with confidence. Remote-first and globally distributed, we work with companies like Nasdaq, DoorDash, and Elastic to secure infrastructure for an AI world. About the Role We are looking for product focused full stack engineers to work on Teleport itself. You will build the user interfaces and backend APIs to implement them for all parts of Teleport. You will work on the full product lifecycle from working with customers to understand their problems, writing RFDs for your proposed design, implementation, testing, and documentation. Teleport is an open source project and most of the code you will write will be open source and written in Go (or Rust), TypeScript, React. What You'll Do - Making Teleport more efficient by adding server-side pagination and filtering capabilities https://github.com/gravitational/teleport/blob/master/rfd/0055-webui-ss-paginate-filter.md - Making Teleport easier to use by designing experiences that guide the user through the setup and onboarding process. - Building user interfaces that support our passwordless https://github.com/gravitational/teleport/blob/master/rfd/0052-passwordless.md vision. What We're Looking For - Go experience - TypeScript and React experience - Strong understanding of web security https://developer.mozilla.org/en-US/docs/Web/Security How We Hire - You will join 30 minute intro call and we will walk you through the compensation, interview process and requirements. - You will join a 30 minute call with the hiring manager, and we will walk you through the take home exercise and help answer questions - You join a Slack channel and submit a coding challenge in Go and TypeScript https://github.com/gravitational/careers/blob/main/challenges/fullstack/challenge.md using GitHub. Why Teleport You're joining a company where the problem is real, the team is small, and your work shows up directly in the product. We're not a big company, you won't get lost in a crowd. You'll have the freedom and autonomy to do what you're great at, alongside teammates who care about doing it right and want to see you succeed. The work is collaborative, there's real room to grow, and the mission is one worth showing up for. Remote-first and globally distributed, we're genuinely passionate about what we're building. The Benefits At Teleport, we believe your career is more important than a list of perks. That's why we focus on making your day-to-day the best it can be — giving you the autonomy, access, and support to do the best work of your career. - Extensive health coverage - Annual expense budget - Rest and recovery policies that maximize your ability to recharge - Investment in your future with retirement savings plans - Professional development opportunities Teleport is an equal opportunity employer and does not discriminate against any employee or applicant on the basis of age, color, disability, gender, national origin, race, religion, sexual orientation, veteran status, or any classifications protected by federal, state, or local law. Candidate Privacy Notice: For information about our collection and processing of job applicant personal data for this position, please see our Job Applicant Privacy Policy and Notice of Collection at goteleport.com/legal/apply/job-applicant/ http://goteleport.com/legal/apply/job-applicant/

Location: New York Workplace: OnSite ABOUT ARCA Arca is a wealth management firm built from the ground up with AI. Most people get financial advice that's reactive: an annual check-in, a plan that's a document instead of a living thing, a relationship where you're one of three hundred clients your advisor is trying to remember. We think that's backwards. The kind of service that used to require a team of specialists behind you, the kind that makes you feel like the only person in the room, should be available to far more than the ultra-wealthy. So we're building it. We're not SaaS — we are the wealth management business, rebuilding it from the inside with AI. Our platform is an Iron Man suit for advisors: it takes over the low-leverage work so they can focus on what actually requires a human, showing up with empathy, context, and judgment. Underneath, it keeps a living understanding of each client. It remembers the thing you mentioned once, six months ago. It notices when your life changes — a new job, a new kid, a market shift — and adjusts before you think to ask. You won't see the technology. You'll just notice your advisor seems to know you better than any financial professional ever has. That's the product we're growing: client by client, on the strength of the experience itself. We started by acquiring firms managing over $1B in client assets, which gave us real advisors, real clients, and real financial outcomes to build against from day one. But acquisition was the starting line. The bet is that an advisor backed by this platform delivers something good enough that clients come to us on their own. It's a $20T market, and we think it's ready to be rebuilt. — Rron, CEO THE RECEIPTS - Stage: Series A, $64M raised - Backed by: General Catalyst, Index Ventures, Venrock - Board & Advisors: Former CEO of Vanguard, Former CFO of Schwab, Founder of Altruist, Morgan Housel (author of The Psychology of Money) THE TEAM We’re small on purpose and engineering heavy: about 80% of the team is technical. We’re based in NYC and hail from high growth startups like Stripe, Ramp, Rippling, Plaid, Doordash, & Glean. We’re fully in-office in Flatiron, five days a week—lunch together, coffee breaks, basketball games, happy hours. THE ROLE As a Member of Technical Staff focused on Platform, you'll own the foundation everything else runs on. The infrastructure, backend systems, and developer experience that let a small engineering team ship agents into a regulated, high-stakes domain without slowing down. One framing we keep coming back to: agents are the primary users of our system of record, and they operate at scale, across every client, all the time. That puts unusual demands on the platform underneath — reliability, cost, and performance aren't afterthoughts, they're the product. If you build it, you own it. WHAT YOU'LL BUILD (AND OWN) - Core infrastructure and backend systems. The services, data stores, and APIs that everything else is built on—designed for a workload where non-human agents are the heaviest, most constant users. This is what lets the rest of the team move fast without the ground shifting under them. - Safe execution for agents. The sandbox where agents write and run code and take real actions, plus the controls that let them reach our backend and databases without doing damage—versioned SDKs, scoped permissions, rate limits, isolation. - Developer experience and internal efficiency. The build, test, deploy, and observability tooling that determines how fast a small engineering team can ship. In a team this small, the leverage of good internal tooling is enormous: every hour you save the team compounds across every acquisition we take on. - Performance and cost. The systems that keep agents fast and correct under real load, and keep our infrastructure spend sane as we scale across four or five acquisitions this year. An agent that's slow, flaky, or ruinously expensive to run isn't shippable. EXAMPLE PROBLEMS YOU'D WORK ON These aren't hypothetical; we're actively working on versions of all of these. - A sandbox where agents can safely write and run code. Our agents don't just call APIs—they write and execute code and take real actions. You'll build the sandbox that makes this safe: how we deploy it, how we version the SDKs agents write against, and how we give agents access to our backend and databases without letting them do damage—rate limits, scoped permissions, isolation. Get this right and agents can do far more, far more safely. - Cloud agents that triage and fix bugs. With a small engineering team shipping into a high-stakes domain, engineering time is our scarcest resource. You'll build agents that live in our infrastructure—triaging incoming bugs, reproducing them, localizing the cause, and proposing or landing fixes—so the team spends its time on problems that actually need a human. Internal leverage that compounds across every acquisition. - Powering the M&A acquisition engine. Growth runs through acquiring RIA firms, and every one arrives with its own stack—a CRM last updated in 2011, a proprietary custodian export, IT systems held together by a decade of habit. You'll build the infrastructure that onboards a new firm and its existing systems onto our platform fast, without a bespoke project every time. The faster and cleaner this gets, the faster we grow. - Querying years of portfolio data at agent speed. Agents need to reason over years of portfolio performance history, and they need it instantly—without hammering the systems that keep the product running. You'll build the read paths, stores, and deep integrations with custodians and performance-reporting providers that let agents query deep history quickly while keeping our backend systems isolated and healthy. - Maturing our backend. The platform is live and proven; now it has to scale across four or five acquisitions this year. You'll level up the foundation—data warehouses, pipelines, and the core systems everything runs on—so it holds up as data volume and agent load grow. THE KIND OF PERSON WHO THRIVES HERE You’re excited by ownership, ambiguity, and building things that matter. - You think in systems and take ownership of the whole stack. You're happiest when you understand how something works end to end, and you instinctively leave infrastructure better instrumented and better understood than you found it. You know "tech debt vs. shipping" is a false tradeoff here—the platform is the thing that lets everyone else ship, so its health is a feature, not a chore. - You're ambitious in a way specific to this work. You're building for people who manage real retirement savings and estate plans. "Good enough" means something different here. You'll rebuild something that works because you know it won't hold through the next acquisition—and that standard motivates you more than shipping faster than last sprint. - You move fast, and you know speed and reliability aren't in tension here. Advisors depend on what you build every day. Breaking prod isn't something you wait until Monday to fix. - You're someone people actually want to be in the room with. This is a small, tight-knit team in one office, five days a week, working on problems that don't have clean answers. The people who thrive here want to argue about architecture at lunch and then build something together in the afternoon. Kind, direct, and low-ego, you can give candid feedback without being an asshole, and you're genuinely energized by this environment (not just tolerant of it). WHY NOW IS THE MOMENT TO JOIN This is a narrow window with unusually high leverage. - We just came out of stealth, and we're growing fast. The team is intentionally small, the equity reflects that, and the ceiling is high. You'd be joining early enough that the work you do now directly shapes what the company becomes and what's possible next. - The advisor-facing product is being defined right now, by a small team that talks to advisors every day. We've just brought on our second firm—the real inflection point, where we start separating what generalizes from what was bespoke. The patterns you set become the foundation for every acquisition that follows. - The consumer product is a blank page. We've never built directly for consumers until now—no legacy, no template. Whoever takes this on defines what the experience even is. - The hard part is behind us, which means the interesting part is starting. The platform is live, the concept is proven, advisors have moved real client assets onto it. The work now is scaling a working system across four or five acquisitions this year—workflows, data, agents, reliability under real load.

Location: Bangalore; EU Remote Workplace: Remote About the role Cyberhaven is redefining data security by tracing how sensitive data actually moves across endpoints, cloud, SaaS, and now AI tools and agents. Our AI Security team builds the backend systems that give enterprises real-time visibility and control over how their data flows into and out of LLMs, GenAI applications, and autonomous agents. We're looking for a Staff/Principal Backend Software Engineer to be one of the most senior technical voices on this team: an early contributor with substantial influence over the architecture of a new and rapidly evolving product line, solving multiple exciting technical challenges along the way. What you'll do - Architect, extend, and optimize a large-scale, highly scalable, fault-tolerant system that handles large graph datasets at enterprise scale processing billions of AI-related events from hundreds of thousands of endpoints in real time, with sub-second latency requirements. - Set technical direction for how Cyberhaven secures data flowing into and out of AI models, GenAI applications, and agents, shaping architecture decisions that will define this product line for years to come. - Architect and implement security features that protect customer data end-to-end. - Solve real-world scaling problems that require thorough performance analysis and troubleshooting. - Work with a modern, constantly evolving microservices based stack including Go, Kubernetes, BigTable, BigQuery, Spanner, Redis, Docker, and Etcd. - Design and build connectors that integrate at the API level with SaaS and AI platforms commonly used in the enterprise (e.g. ChatGPT, Copilot, and other GenAI tools). - Write secure-by-design, hardened software that withstands real-world attacks while processing untrusted data and communicating with hundreds of thousands of endpoints over the internet. - Raise the technical bar across the team through architecture reviews, mentorship of senior/staff engineers, and setting patterns others follow. Who you are - You have a strong track record building backends that are actively used at scale, and you've contributed significantly to and extended their architecture, not just shipped features. - You have deep, hands-on experience developing large applications in Go. - You've operated databases at very large scale. Knowledge of GCP services (BigTable, BigQuery, Spanner) is a plus. - You have experience profiling and optimizing distributed code that uses large databases and streaming platforms. - Experience building connectors that integrate at the API level with SaaS applications commonly used in the enterprise is a plus, especially with AI/LLM platforms. - You have hands-on production experience with microservices and Kubernetes. - You have excellent verbal and written communication skills in English, and can be available to frequently communicate with distributed teams in Europe and the US. - You thrive in fast-paced environments tackling ambiguous challenges, and you enjoy shipping with velocity. - You have a customer-centric mindset and enjoy direct feedback from customers. - You are based in India or the EU. Joining Cyberhaven means joining the team building Data Security for the Agentic Enterprise. Traditional tools fall short. Cyberhaven traces the full lifecycle of your data, adapting protection to changing context, so protection moves with the way people actually work, not against it. AI Changed Work. We Protect It. Backed by $250M from leading investors like Khosla and Redpoint, our team includes leaders who built industry-defining technologies at CrowdStrike, Palo Alto Networks, Meta, Google, and more. This role lets you shape the future of data security, working alongside people driven to protect workflows, not just data, for customers who cannot afford to slow down to stay safe. AI Tools Disclosure: As part of Cyberhaven's hiring process, we use recruiting tools that include AI-powered features to help with tasks like scheduling, note-taking, workflow automation, and other recruiting activities ("AI Tools"). The AI Tools may process information you provide during the application and interview process. When AI-assisted interview note taking is used, candidates are notified in advance and have the option to opt out of the AI-assisted interview note taking. Cyberhaven is committed to creating a diverse environment and is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.

Задачи: — Переписать существующее iOS-приложение (Swift) на Android. — Полностью реализовать существующий функционал. — Интегрироваться с готовым Backend API. — Поддерживать и развивать Android-версию приложения. Требования: — Опыт коммерческой Android-разработки от 1 года. — Уверенное знание Kotlin. — Опыт разработки приложений под Android. — Опыт работы с REST API. — Понимание архитектуры мобильных приложений. — Умение писать чистый и поддерживаемый код. Будет плюсом: — Jetpack Compose. — Firebase. — Опыт публикации приложений в Google Play. — CI/CD и опыт настройки production-сборок. Условия: — Формат работы: гибрид, 5/2.

Location: Yerevan, Armenia Position Summary We are looking for an experienced Senior Automation QA Engineer to join our Quality Engineering team. In this role, you will be responsible for ensuring the quality, reliability, and performance of our products by designing, developing, and maintaining scalable automation solutions. You will work closely with Software Engineers, Product Managers, and QA Engineers while contributing to automation strategy, quality improvements, and engineering best practices. Responsibilities Develop, maintain, and improve backend automated tests using C#/.NET Develop and maintain frontend automated tests using Playwright (JavaScript/TypeScript) Design and execute automated API, UI, integration, and regression test suites Validate REST APIs, WebSocket services, and database data Design, execute, and maintain performance and load tests using JMeter Create, maintain, and manage test data for automation and testing activities Collaborate closely with Manual QA Engineers and Software Engineers throughout the SDLC Participate in test planning, estimation, test strategy, and test case design Maintain and manage test cases using TestRail Track defects and collaborate with development teams using Jira Utilize AI tools to improve QA processes, productivity, and test automation workflows Create and maintain AI helper agents to support testing activities Continuously improve automation frameworks, testing processes, and quality standards Requirements 3+ years of experience in Automation Quality Assurance Strong understanding of QA processes, SDLC, and STLC Experience working in Agile/Scrum environments Strong analytical, problem-solving, and debugging skills Ability to work independently and collaboratively within cross-functional teams Proficiency in technical English Mentorship experience is considered a plus Technical Skills Strong experience with C#/.NET for backend automation Hands-on experience with Playwright using JavaScript or TypeScript Strong understanding of Object-Oriented Programming (OOP) principles Experience with automation frameworks such as NUnit or MsTest Hands-on experience with REST API and WebSocket automation Strong SQL knowledge, including complex queries, joins, and data validation Experience with JMeter for performance and load testing Experience with API testing tools such as Postman Experience with debugging/proxy tools such as Charles Proxy or Fiddler Good understanding of Git version control Experience with TestRail or similar test management systems Experience with Jira or similar project management tools Experience using AI tools (e.g., ChatGPT, GitHub Copilot, Claude, Gemini) in QA processes Experience creating and utilizing AI helper agents for QA activities

Location: Palo Alto, California, United States 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 As a Senior Machine Learning Engineer on the Applied AI team at Nebius Token Factory, you will own model and endpoint optimization from model artifacts through production deployment. Your work will span model internals, quantization and model compression, speculative decoding, KV-cache optimization, inference engines, serving architecture, and benchmarking to improve latency, throughput, memory efficiency, GPU utilization, and cost per token while preserving model quality and reliability. You will work with kernel and platform engineers to investigate serving problems, compare configurations, and resolve performance and quality regressions. You will evaluate model-and engine-level optimizations together with distributed inference designs, including request routing, scheduling, prefill/decode coordination, and multi-node GPU execution. Your improvements will be validated through reproducible benchmarks and safe production rollouts under real-world workloads. Your responsibilities : Own optimization projects for specific model families, customer endpoints, or serving backends. Compare inference engines and recommend practical serving configurations suited to individual workloads. Diagnose model-quality and performance regressions during production rollouts. Improve LLM and VLM endpoint latency, throughput, memory efficiency, GPU utilization, quality, and cost per token. Deploy, configure, benchmark, and extend inference engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, and NVIDIA Dynamo. Develop production model-compression workflows covering quantization, quantization-aware training, distillation, low-bit serving, and accuracy recovery. Implement or integrate speculative decoding, draft-model approaches, KV -cache optimization, prefix caching, chunked prefill, continuous batching, and disaggregated prefill/decode serving. For prefill/decode disaggregation (PDD), evaluate KV-cache transfer, worker placement, and capacity balancing to determine which workloads benefit from the architecture. Design and improve LLM request routers and scheduling policies that balance worker utilization queuing, request characteristics, and KV-cache locality while meeting latency and reliability targets Scale dense and mixture-of-experts inference across multiple GPU nodes. Select and tune tensor, pipeline, data, and expert parallelism - including wide expert parallelism (WideEP) - with attention to hardware topology, expert load balance, and communication overhead. Build reproducible benchmark harnesses measuring for TTFT , TPOT , tokens per second per GPU, p95/p99 latency, GPU memory, reliability, and cost per token. Validate architecture choices under representative traffic and consistent GPU budgets. Collaborate with GPU kernel engineers and platform engineers to trace bottlenecks across model code, kernels, runtime, scheduler, gateway, and cluster layers. Produce design documents, performance reports, rollout plans, and customer-facing technical explanations. Must-haves : Strong engineering skills in Python and PyTorch. Hands-on experience deploying or optimizing LLM, VLM , or high-throughput transformer inference. Practical knowledge of at least one modern inference stack, such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, Ray Serve, KServe, or an equivalent internal system. A strong understanding of transformer inference bottlenecks involving KV cache, attention, memory bandwidth, batching, parallelism, and long-context serving. Ability to quantify trade-offs among latency, throughput, quality, utilization, and cost. Experience designing or optimizing distributed inference system architecture, with hands-on work in one or more areas such as request routing, distributed scheduling, PDD, multi-node inference, or expert parallelism. Strong communication skills and collaboration skills across research, kernel, infrastructure, product, and customer teams. Nice - to - have s : Experience with quantization-aware training, post-training quantization, FP8 , INT8 , INT4 , NVFP4 , MXFP4 , AWQ , GPTQ , SmoothQuant. Experience with distillation, speculative decoding, EAGLE, Medusa, multi-token prediction, or related inference acceleration methods. Experience supporting agentic workloads involving including tool calling, structured outputs, streaming APIs, high concurrency, and multi-step orchestration. Familiarity with CUDA or Triton; the role does not require kernel engineering to be the candidate's primary specialization. Contributions to open-source projects such as vLLM, SGLang, TensorRT-LLM, FlashInfer, LMCache, PyTorch, Triton, Ray, or KServe. Implementation experience with cache-aware request routing, PDD, or WideEP, including diagnosing communication bottlenecks and load imbalance. Familiarity with GPU communication libraries and interconnects, such as NCCL, NVLink, Infiniband, or RoCE. Key employee benefits in the US: Health insurance: 100% company-paid medical, dental, and vision coverage for employees and families. 401(k) plan: Up to 4% company match with immediate vesting. Parental leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers. Remote work reimbursement: Up to $85/month for mobile and internet. Disability & life insurance : Company-paid short-term, long-term and life insurance coverage. #LI-BH3 Pay Transparency We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law. Base Compensation Range $195,200 — $262,200 USD 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: Palo Alto, California, United States 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 - have s : 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. Key employee benefits in the US: Health insurance: 100% company-paid medical, dental, and vision coverage for employees and families. 401(k) plan: Up to 4% company match with immediate vesting. Parental leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers. Remote work reimbursement: Up to $85/month for mobile and internet. Disability & life insurance : Company-paid short-term, long-term and life insurance coverage. Pay Transparency We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law. Base Compensation Range $195,200 — $262,200 USD 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: In-Office About Us At Cloudflare, we are on a mission to help build a better Internet. Today the company runs one of the world’s largest networks that powers millions of websites and other Internet properties for customers ranging from individual bloggers to SMBs to Fortune 500 companies. Cloudflare protects and accelerates any Internet application online without adding hardware, installing software, or changing a line of code. Internet properties powered by Cloudflare all have web traffic routed through its intelligent global network, which gets smarter with every request. As a result, they see significant improvement in performance and a decrease in spam and other attacks. Cloudflare was named to Entrepreneur Magazine’s Top Company Cultures list and ranked among the World’s Most Innovative Companies by Fast Company. At Cloudflare, we’re not looking for people who wait for a polished roadmap; we’re looking for the builders who see the cracks in the Internet that everyone else has simply learned to live with. We value candidates who have the instinct to spot a "normalized" problem and the AI-native curiosity to create a solution using the latest tools. Our culture is built on iteration, leveraging AI to ship faster today to make it better tomorrow, while ensuring that every improvement, no matter how small, is shared across the team to lift everyone up. If you’re the type of person who values curiosity over bureaucracy, and that AI is a partner in solving tough problems to keep the Internet moving forward, you’ll fit right in. Available Locations: Austin, TX | New York, NY | San Francisco, CA About the Role AI inference is becoming core infrastructure. Every serious application will need access to many models, across many providers, with reliability, observability, security, cost control, and routing built in from the start. AI Gateway is Cloudflare’s bet that this layer should exist at the network edge: close to users, close to compute, and simple enough that a developer can adopt it with one API change. We are looking for a Senior Systems Engineer to help build that layer. This is a deeply hands-on individual contributor role for someone who can move from a half-formed product idea to a working, production-ready system quickly. You will work across the stack — from distributed systems and high-throughput APIs to developer-facing workflows, dashboards, SDKs, and docs — helping define how developers build and operate AI applications on Cloudflare. You should be excited by the current pace of the AI ecosystem. Provider APIs change weekly, new models appear constantly, and developer expectations are rising just as quickly. AI Gateway needs engineers who can build durable infrastructure in that environment without slowing down. We also believe AI should change how we build software, not just what we build. We dogfood AI Gateway, Workers AI, and Cloudflare’s broader agent stack heavily. You should be comfortable using AI-native tools, agents, harnesses, and automation to improve your own engineering loop — and interested in building the internal tools and context that help a small team ship like a much larger one. Responsibilities Build core AI Gateway systems that handle high-volume inference traffic across providers, models, and Cloudflare’s global network. Design and implement APIs, routing primitives, observability features, controls, and developer workflows that make AI Gateway reliable and easy to adopt. Work across product and infrastructure boundaries with Workers AI, Agents, and the broader Developer Platform organization to make Cloudflare’s AI platform feel like one coherent product. Turn ambiguous product and technical ideas into prototypes, design docs, shipped features, and measurable production improvements. Improve reliability, performance, cost efficiency, and debuggability for latency-sensitive AI workloads. Build the software — and the software that builds the software: agents, tests, harnesses, evaluation loops, operational tools, and internal context that help the team move faster with confidence. Sweat the developer experience. Error messages, SDKs, dashboard flows, docs, examples, and API details all matter. Mentor other engineers through design review, code review, debugging, and technical leadership without needing people-management authority. Bring strong opinions, clear judgment, and a willingness to revise your view when data, users, or production reality prove otherwise. Desirable Skills, Knowledge & Experience 5+ years of experience building and operating production systems, ideally in AI infrastructure, developer platforms, cloud infrastructure, edge/proxy systems, high-scale APIs, or distributed systems. Strong systems engineering fundamentals: networking, APIs, reliability, observability, data modeling, performance, and operational tradeoffs. Experience designing or scaling distributed systems where latency, throughput, availability, and correctness all matter. Ability to work comfortably across the stack, from API design and product surfaces to backend services, queues, storage systems, control planes, or performance-critical code. Strong developer tools taste. You have used and built enough APIs, SDKs, dashboards, CLIs, docs, and internal platforms to know what makes them delightful or painful. Clear technical communication: you can write design docs, explain tradeoffs, review architecture, and align engineers and product partners around a practical path forward. A builder’s bias toward working software. You can prototype quickly, ship incrementally, and improve systems while they are already in production. Operational judgment. You understand that at Cloudflare scale, rare bugs are not rare for long, and you build systems that are observable, resilient, and recoverable. Comfort using AI-native development workflows and curiosity about how agents, tools, and automation can improve engineering productivity. Bonus Points Experience building AI/ML infrastructure, inference platforms, model gateways, LLM-powered applications, or provider abstraction layers. Experience building developer-facing APIs, SDKs, CLIs, dashboards, or platform products at scale. Hands-on experience with proxy, edge, gateway, routing, or traffic management systems. Familiarity with the current AI ecosystem: model providers, model capabilities, agent frameworks, inference patterns, and the rough edges developers hit in practice. Experience with cost controls, rate limits, caching, retries, fallbacks, evaluation, or observability for AI workloads. Experience with TypeScript, Go, Rust, or similar systems/backend languages. You have built or heavily customized your own agentic development environment: custom agents, harnesses, tools, or workflows that changed how you ship. You have built something with AI Gateway, Workers AI, or a competing product, and have opinions about what is missing. A track record of writing publicly about developer tools, infrastructure, systems, or AI products you have built. Compensation Compensation may be adjusted depending on work location. For Bay Area based hires: Estimated annual salary of $194,000 - $266,000 For New York City, New Jersey, Washington, Washington DC, and California (excluding Bay Area) based hires: Estimated annual salary of $185,000 - $254,000 Equity This role is eligible to participate in Cloudflare’s equity plan. Benefits Cloudflare offers a complete package of benefits and programs to support you and your family.  Our benefits programs can help you pay health care expenses, support caregiving, build capital for the future and make life a little easier and fun!  The below is a description of our benefits for employees in the United States, and benefits may vary for employees based outside the U.S. Health & Welfare Benefits Medical/Rx Insurance Dental Insurance Vision Insurance Flexible Spending Accounts Commuter Spending Accounts Fertility & Family Forming Benefits On-demand mental health support and Employee Assistance Program Global Travel Medical Insurance Financial Benefits Short and Long Term Disability Insurance Life & Accident Insurance 401(k) Retirement Savings Plan Employee Stock Participation Plan Time Off Flexible paid time off covering vacation and sick leave Leave programs, including parental, pregnancy health, medical, and bereavement leave What Makes Cloudflare Special? We’re not just a highly ambitious, large-scale technology company. We’re a highly ambitious, large-scale technology company with a soul. Fundamental to our mission to help build a better Internet is protecting the free and open Internet. Project Galileo : Since 2014, we've equipped more than 2,400 journalism and civil society organizations in 111 countries with powerful tools to defend themselves against attacks that would otherwise censor their work, technology already used by Cloudflare’s enterprise customers--at no cost. Athenian Project : In 2017, we created the Athenian Project to ensure that state and local governments have the highest level of protection and reliability for free, so that their constituents have access to election information and voter registration. Since the project, we've provided services to more than 425 local government election websites in 33 states. 1.1.1.1 : We released 1.1.1.1 to help fix the foundation of the Internet by building a faster, more secure and privacy-centric public DNS resolver. This is available publicly for everyone to use - it is the first consumer-focused service Cloudflare has ever released. Here’s the deal - we don’t store client IP addresses never, ever. We will continue to abide by our privacy commitment and ensure that no user data is sold to advertisers or used to target consumers. Sound like something you’d like to be a part of? We’d love to hear from you! Please note that applicants who progress to the offer stage of the interview process may be asked to attend an in-person interview within one of the Cloudflare Offices or Cloudflare Hubs.  More details about this will be available at that stage of the interview process. This position may require access to information protected under U.S. export control laws, including the U.S. Export Administration Regulations. Please note that any offer of employment may be conditioned on your authorization to receive software or technology controlled under these U.S. export laws without sponsorship for an export license. Cloudflare is proud to be an equal opportunity employer.  We are committed to providing equal employment opportunity for all people and place great value in both diversity and inclusiveness.  All qualified applicants will be considered for employment without regard to their, or any other person's, perceived or actual race, color, religion, sex, gender, gender identity, gender expression, sexual orientation, national origin, ancestry, citizenship, age, physical or mental disability, medical condition, family care status, or any other basis protected by law. We are an AA/Veterans/Disabled Employer. Cloudflare provides reasonable accommodations to qualified individuals with disabilities.  Please tell us if you require a reasonable accommodation to apply for a job. Examples of reasonable accommodations include, but are not limited to, changing the application process, providing documents in an alternate format, using a sign language interpreter, or using specialized equipment.  If you require a reasonable accommodation to apply for a job, please contact us via e-mail at hr@cloudflare.com or via mail at 101 Townsend St. San Francisco, CA 94107.