Вакансии

От 4 лет опыта коммерческой разработки, из них от 2 лет на Go (HTTP/gRPC, конкурентность, тестирование).

Задачи: — Внедрение и развитие практик agent-engineering в разработке программного обеспечения. — Участие в реальных задачах разработки, построение и развитие агентского слоя, подготовка agent harness. — Внедрение SDD и других подходов AI-assisted SDLC. — Помощь командам эффективно и безопасно использовать код-агентов. — Формирование внутренних стандартов и лучших практик. — Превращение AI-кодинга в управляемый производственный процесс с использованием спецификаций, контекста, тестов, ревью, observability, permissions и качественного результата. — Формирование целевой архитектуры на основе бизнес-требований и существующих систем. — Выбор технологий, границ компонентов и интеграционных подходов. — Оценка нефункциональных требований: масштабируемость, надёжность, безопасность, стоимость. — Подготовка артефактов: схемы, ADR, интеграционные контракты. — Согласование решений с командными и предметными архитекторами и стейкхолдерами. — Сопровождение команд при реализации. — Участие во всех этапах AI SDLC: от требований и декомпозиции до реализации, тестирования, ревью и релиза. — Разработка и сопровождение задач с использованием AI-код-агентов. — Подготовка agent-ready спецификаций, task breakdown, acceptance criteria и context packs. — Построение и развитие agent harness: инструкции, шаблоны, инструменты, MCP-интеграции, тестовые контуры, evals, observability. — Внедрение SDD, TDD/BDD и других подходов к AI-assisted разработке. — Настройка безопасного контура работы агентов: sandbox, permissions, approval gates, audit trail. — Развитие code-RAG / context engineering по внутренним репозиториям и документации. — Помощь командам во внедрении AI-assisted workflows в реальные процессы. — Проведение воркшопов, менторинг разработчиков, подготовка внутренних инструкций. — Оценка новых AI coding tools, моделей и агентских фреймворков. — Измерение эффекта внедрения и улучшение практик на основе данных. Требования: — Опыт разработки не менее 5 лет, из них 2–3 года в архитектуре распределённых систем. — Глубокие знания микросервисов, интеграций (REST, Kafka, ESB), баз данных и облачных платформ. — Владение методами моделирования архитектуры: C4, UML, ArchiMate. — Понимание вопросов безопасности, отказоустойчивости, планирования ёмкости и оценки стоимости. — Знание корпоративных стандартов, EAM-ландшафта и предметной области. — Навыки коммуникации и ведения переговоров, умение обосновывать решения для бизнеса и разработки. Условия: — Удалённая работа в формате fulltime. — Зарплата до 376 000 рублей netto (ИП, самозанятый).

• Развивать публичное OpenAI/Anthropic‑совместимое API, медиамодельные и транскрибирующие эндпоинты, обеспечивать стабильность и мониторинг. • Подключать новые LLM и медиамодели в течение нескольких часов после релиза.

Location: Москва SberTech — одна из крупнейших IT-компаний в России. Наш основной заказчик — Сбер, но мы стремимся создавать высоконадежные (99.99) продукты, востребованные на рынке в разных сферах бизнеса. Мы развиваем _Platform V Synapse_ — интеграционную платформу Сбера. Это облачное решение для интеграции и миграции сервисов, обрабатывающее миллионы запросов. Наши продукты являются базовым технологическим слоем для внедрения agentic ai в части всех взаимодействий агентов и служат enabler’ми для AI-трансформации бизнеса. Мы — команда, сочетающая опыт и глубокие знания с активной позицией. Работаем по Agile, используем собственный производственный конвейер. Автоматизированное тестирование и CI/CD — основа нашего процесса разработки. Мы способствуем развитию экосистемы Сбербанка, создавая важные интеграционные элементы. Но не ограничиваемся им – наши продукты широко известны на рынке Российского ПО. Обязанности * проектирование и развитие архитектуры GenAI-решений от прототипа до production, выбор паттернов (prompt-based, RAG, агенты, fine-tuning) * построение LLM-пайплайнов: RAG (chunking, embeddings, reranking), управление контекстом, tool use / function calling * внедрение LLMOps-практик: observability, трассировка, guardrails, тестирование качества * участие в проектировании архитектуры и разработке микросервисов и базовых компонентов платформы (backend, микросервисы, cloudnative) * руководство командой сотрудников, управлением процессом производства ПО: декомпозиция, delivery, code и architecture review, инженерные стандарты, управление качеством * работа с продуктом: анализ потребностей пользователей, полный цикл работы с продуктовыми гипотезами с ИИ-составляющей. Требования * опыт коммерческой разработки от 5 лет: backend, микросервисы, K8s, желательно Golang * опыт управления командой разработки от 2 лет * опыт в AI/ML и GenAI с кейсами вывода решений в ПРОМ * опыт применения паттернов проектирования и принципов построения высоконагруженной микросервисной архитектуры * опыт реализации AI-агентов (LangChain / LangGraph / ReAct / MCP / A2A) * применение практик работы с LLM: ограничения, галлюцинации, context window management. ### Будет плюсом опыт: * проектирования мульти-агентных систем и сложной оркестрации * работы с временными рядами, аномалиями, прогнозированием * работы с Istio Service Mesh, разработки Kubernetes-операторов и сетевых решений * внедрения AI-практик и решений в платформенные инструменты, инфраструктурные решения и PaaS-сервисы. Условия * гибридный формат работы * годовой бонус и ежегодный пересмотр зарплаты * статус аккредитованной ИТ-компании * расширенный ДМС с первого дня и льготное страхование для семьи * корпоративный университет Сбера, внутренняя образовательная платформа, участие в IT-конференциях * 90 дней удаленной работы из любого региона РФ * льготная ипотека в Сбере, подписка Прайм с возможностью совместного использования на трёх близких.

О роли: Мы ищем Middle+/Senior Fullstack-разработчика, который заберет под ключ направление публичного API GPTunneL (OpenAI/Anthropic-совместимое API, медиамодели, биллинг, ЛК). Что предстоит делать: Публичное API: Развивать OpenAI/Anthropic-совместимое API, API медиамоделей и транскрибации.

Разрабатывать и развивать интеграцию 1С с AI-агентами через протокол MCP.

Location: Bangalore, India GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. An overview of this role As a Data Engineer at GitLab, you'll build, run, and maintain the pipelines and infrastructure that bring data from our enterprise applications, internal systems, and GitLab's software as a service (SaaS) platform into our data platform. Your work will power company reporting, data science, and artificial intelligence (AI) self-service. You'll own features end to end, from source integration to tested, monitored, documented data ready for use. You'll focus on keeping the platform reliable, secure, and cost-efficient as data volumes and AI usage grow, and making governed data ready for AI tools, agents, and Model Context Protocol (MCP)-based access. This is an AI-first role: you'll use AI coding agents every day while maintaining high standards for code review and testing. You'll report to the Director, Data Platform. What you’ll do Build, extend, and maintain extract, load, transform (ELT) pipelines from enterprise applications such as Salesforce, Zuora, Zendesk, and Marketo, internal systems, and the GitLab SaaS platform. Integrate data through application programming interfaces (APIs) and event sources such as Snowplow. Manage and maintain the infrastructure behind the platform, including SaaS tools such as Snowflake, Fivetran, and Monte Carlo, and self-managed services such as Airflow and Kubernetes. Write maintainable, well-tested, performant Python and Structured Query Language (SQL) code that meets our internal standards, and review GitLab team members' merge requests. Own day-to-day platform operations: triage and resolve failed pipelines, infrastructure issues, and GitLab team member requests. Use monitoring, service-level agreements (SLAs), data contracts, query and warehouse tuning, and data quality checks to keep the platform reliable and cost-efficient. Partner with Analytics Engineers, Data Analysts, Data Scientists, Data Governance, and business partners so data lands in the shape they need for downstream use. Use AI coding agents such as Claude Code, OpenCode, GitLab Duo, or similar tools to speed up development, debugging, testing, and documentation without compromising review rigor. Apply role-based access control (RBAC) and masking policies to make governed data available to AI tools and agents. Handle data subject to Sarbanes-Oxley (SOX) controls, material nonpublic information (MNPI), and personal data safely. Ship medium-sized features independently in small, iterative merge requests, and document architecture and runbooks in the handbook. What You'll Bring Professional experience shipping production data pipelines in Python. Deep, hands-on SQL skills and experience with a cloud data warehouse; Snowflake is preferred. Hands-on experience with ELT or extract, transform, load (ETL) pipelines at scale, including incremental loads, schema changes, backfills, and API ingestion. Experience with orchestration tools such as Airflow, familiarity with dbt, and solid data modeling fundamentals, including Kimball dimensional modeling. Experience running and troubleshooting data infrastructure, with comfort using Git-based workflows, continuous integration and continuous delivery (CI/CD), and code review. Hands-on experience using AI coding tools in your daily work, with an understanding of where they help and where they fall short. Clear written communication, the ability to use GitLab, and comfort working asynchronously in a fully remote organization in line with GitLab's values. Preferred, not required: experience with Snowflake; Docker, Kubernetes, Google Cloud Platform (GCP), or Terraform; SaaS application APIs such as Salesforce, Zuora, or Zendesk; data observability tooling; or Snowflake administration. About the team The Data Platform team within Enterprise Data is responsible for a scalable, reliable and secure data platform for data analytics at GitLab. That includes managing and maintaining the infrastructure we use, both SaaS and self-managed, and building and maintaining the data pipelines that bring data into it. We make data reliable, secure and ready for analytics, data science and AI tools. As more questions go straight to AI tools and agents, the platform underneath has to be more reliable, better governed and more cost-efficient than ever. We're fully remote and globally distributed, and work asynchronously through issues, merge requests and the handbook. How GitLab Supports Full-Time Employees Benefits to support your health, finances, and well-being Flexible Paid Time Off Team Member Resource Groups Equity Compensation & Employee Stock Purchase Plan Growth and Development Fund Parental Leave Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application. Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process. Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us. GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law . If you have a disability or special need that requires accommodation , please let us know during the recruiting process .

-Разработка и внедрение AI-агентов для автоматизации внутренних процессов (чат-боты, интеллектуальная обработка документов, автономные системы). -Проектирование, внедрение и оптимизация RAG-систем (поиск по базам знаний, корпоративным данным, векторные хранилища).

-Разрабатывать AI-агентов и мультиагентные сценарии для автоматизации различных этапов PDLC. -Развивать Ассистента платформы на базе LLM.

Partner with Account Executives and Solutions Engineers on strategic enterprise opportunities involving AI architecture, governance, integrations, and complex technical requirements. 5+ years of experience in enterprise architecture, solution architecture, sales engineering, consulting, AI strategy, or a comparable customer-facing technical leadership role.

Location: Москва Наша команда ищет Senior Data Scientist, который будет заниматься разработкой ключевых GenAI-инициатив в канале ВСП. Вам предстоит с нуля выстраивать end-to-end пайплайны: от исследования и прототипирования до вывода в продакшен интеллектуальных агентов и ML-моделей. Вы будете отвечать за полный цикл создания ценности — от бизнес-гипотезы до работающего продукта. Обязанности **Что предстоит делать:** * Проектировать и внедрять в продакшен мультиагентные системы (orchestrator–workers, ReAct, Plan-and-Execute). * Строить продакшен-RAG (гибридный поиск, ANN/HNSW, query rewriting, GraphRAG). * Разворачивать harness для агентов, настраивать guardrails, трейсинг траекторий и проводить A/B-тесты версий (промпт/модель/инструменты). * Оптимизировать LLM-пайплайны (DSPy / MIPROv2, управление контекстом и памятью). * Дообучать модели (SFT, LoRA/QLoRA) под ру-домены и внедрять function calling / MCP. Требования * **Релевантный опыт работы от 5 лет** * **Агенты:** LangChain, LangGraph (обязательно); LlamaIndex, CrewAI (опционально). * **ML/NLP:** трансформеры, attention, prompt engineering (CoT, few-shot), Fine-tuning. * **RAG и поиск:** Qdrant, Elasticsearch или FAISS. * **Оценка:** LLM-as-a-Judge, Inspect AI / RAGAS / DeepEval, матстатистика для A/B-тестов. * **Классический ML:** CatBoost, XGBoost, скоринг, предотвращение утечек. * **MLOps:** Python (продакшен-код, ООП), Docker, k8s, CI/CD, MLflow/WandB, мониторинг data drift. * **Инференс:** vLLM, Triton, TensorRT/ONNX, квантизация, оптимизация latency и стоимости. Условия * Гибкий гибрид (обсуждаем индивидуально). * Современный IT-офис вблизи Москва-Сити с фитнес-залом. * Ипотека с выгодой для сотрудника и льготные условия кредитования. * Бесплатная подписка СберПрайм+. * Скидки на продукты компаний-партнеров. * ДМС с первого дня и льготное страхование для близких. * Корпоративная пенсионная программа. * Обучение за счет компании: онлайн-курсы в онлайн-школе Сбера и неограниченный доступ к библиотеке, обучение в Корпоративном университете, тренинги, митапы и возможность получить новую квалификацию. * Крупнейшее DS&AI Community — более 600 DS-банка, регулярный обмен знаниями, опытом и лучшими практиками, интерактивные лекции и мастер-классы от ведущих ВУЗов и экспертов технологических компаний, дайджест о самых последних разработках в области DS&AI и отчеты с крупнейших конференций мира.

-Участие в проектировании и разработке новой платформы. -Разработка, оптимизация и сопровождение высококачественного кода, устранение дефектов и технических долгов.

• Сбор и формализация требований (user story, use case, BPMN/UML), написание ТЗ/ФТ, постановка задач в Jira.

Location: Remote, Global Workplace: Remote 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 Software Engineer, AI Tooling Infrastructure to join our AI Tooling team and help build and run the backend services behind Supabase’s AI products. The Supabase MCP server is one example of what the team runs in production, alongside the services that support agent skills and the dashboard assistant. You’ll work closely with the Platform, SRE, Auth, Billing and Edge Functions teams, and with the engineers on the AI Tooling team who build the product surfaces. The AI Tooling team runs its own services in production and carries its own on-call. Traffic from AI clients and agents behaves differently from traffic from people: hundreds of calls a minute, retry loops, and no reading of deprecation warnings. This role makes sure the services they use are reliable, correctly authorized and observable. It is a systems and infrastructure role rather than a product AI role, and it is not an SRE role. You will build and operate these systems, not only advise on them. This role is ideal for someone who thrives in async, fast-paced environments, owns what they ship in production, and is excited about building developer tools that scale to millions. WHAT YOU’LL BE RESPONSIBLE FOR In this role, you’ll: - Build and operate the production backend services behind Supabase’s AI products, including the MCP server: deployment, scaling, configuration and release. - Take ownership of how those services are structured, including splitting or reshaping a service when that makes it safer to run and easier to change. - Define, measure and meet reliability targets for the services the team runs. Set SLOs with the SRE team, track error budgets, and use them to decide what to fix first. - Participate in the AI Tooling team on-call rotation, and be a first responder for incidents in those services. Investigate, fix, coordinate with other teams during incidents, and write the follow-ups and runbooks. - Work the AI Tooling team support queue alongside on-call, and turn recurring issues into fixes rather than repeated manual answers. - Own authentication and authorization for AI and agent clients: OAuth apps, scoped tokens, and permissions that make sense for agents as well as humans, including how we change or deprecate a permission without breaking existing integrations. - Own observability for the services the team runs: metrics, traces, logs, dashboards and alerts, so we find problems before customers do. - Handle capacity and abuse for agent traffic: rate limits, cost and safety controls, and behavior under load or when a dependency degrades. - Improve the architecture of the services the team owns as they grow, including splitting or reshaping a service when that makes it safer to run and easier to change. - Work with the Platform team on shared code, review ownership and release process, and with the SRE team on infrastructure, alerting and incident practice. - Contribute to the code of the AI Tooling team’s tools, such as the MCP server, where it affects reliability and security, in partnership with the engineers who own the product surface. YOU MIGHT BE A GOOD FIT IF YOU Must have: - Have 5+ years of experience building and running backend services in production, and have carried on-call for them. - Write strong TypeScript or Go, and are comfortable working in the other. - Have designed and operated APIs with real consumers: versioning, error handling, rate limiting, and backwards-compatible change. - Know OAuth, OIDC, JWT and API authorization well enough to debug them in production and to explain where implementations commonly deviate from the spec. - Have a good understanding of PostgreSQL and relational databases. - Are comfortable with CI/CD, Docker and containerized deployments, and with infrastructure as code. Any tool is fine. - Can debug across services: follow a slow or failing request through several systems using metrics, traces and logs. - Communicate clearly in writing, work with a wide range of stakeholders, and thrive in async, globally distributed teams. - Are comfortable navigating ambiguity and moving quickly. Pick your centre of gravity. We expect you to be deep in at least one of these, and to tell us which: - APIs and auth: You have designed permission models, token scopes or OAuth flows for third-party or machine clients, and you have migrated them without breaking users. - Operations: You have run production services yourself: carried the pager, written alerts and runbooks, used SLOs or similar to decide what to fix, and made shared systems less risky over time. - Gateways and distributed systems: You have built or run proxies or gateways, or debugged hard failures across services (retries, partial failures, upstream outages, fail-open vs fail-closed decisions), and you can reason about load and degradation. Nice to have: - Experience with agent tooling (MCP or similar), or serving traffic from AI agents and LLM clients. - Experience moving a service out of a larger codebase while keeping existing clients working. - Experience with Grafana, OpenTelemetry, Logflare or a similar observability stack, and real SLO work. - Open-source contributions, especially to Node.js, Go, TypeScript or API tooling. 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: Remote, Canada; Remote, United States GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster. The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems. Co-create the future with us as we build technology that transforms how the world develops software. * Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab. As an Intermediate Backend Engineer on the GitLab Orbit team, you'll help build and operate a graph data service that supports GitLab Duo agents, analytics, and architecture-level features across GitLab.com, Dedicated, and Self-Managed deployments. You'll join a Rust-first team that values clear ownership, thoughtful system design, and rigorous thinking about data and reliability. The Knowledge Graph service is a Rust backend that builds a property graph from GitLab’s software development lifecycle (SDLC) and code data. It uses ClickHouse, NATS JetStream, and the Data Insights Platform. It exposes secure graph queries and MCP tools used by AI agents and product features. In this role, you’ll deliver features and improvements in well-scoped areas, learn the broader architecture, and contribute to reliability, observability, and operational readiness. In your first year, you’ll take clear ownership of specific components or features (for example, parts of the SDLC indexing pipeline or query paths). You’ll help reduce single points of failure with better tests and runbooks, and you’ll help the team ship analytical services that are easier to maintain and evolve over time. What you’ll do Implement and iterate on backend features in the Rust-based Knowledge Graph service, including changes to the query engine, SDLC and code indexing flows, and API endpoints (including MCP endpoints) under guidance from senior and staff engineers. Maintain integrations between Knowledge Graph and the rest of the GitLab platform, working in areas that touch GitLab Rails, the Data Insights Platform (Siphon, NATS, ClickHouse), and GitLab Duo Agent Platform. Contribute to system design discussions by proposing options, raising questions, and documenting decisions, with a focus on reliability, scalability, and maintainability for analytical graph workloads. Improve the operational maturity of the service by adding or enhancing metrics, logging, runbooks, alerts, and small readiness tasks, and by participating in on-call rotation as appropriate for your level and experience. Collaborate asynchronously with product, data, infrastructure, security, and AI counterparts to clarify requirements, align on scope, and ship features safely for customers and sustainably for the team. Use AI-assisted development workflows responsibly (for example, using Knowledge Graph-backed agents and internal Duo tooling), and share what works with the team while keeping a strong focus on code quality and correctness. Participate in code reviews, knowledge-sharing sessions, and pairing to both learn from others and help maintain consistent standards across the codebase. Contribute across the stack when needed, including occasional Ruby work for Rails integration and authorization paths, or small frontend changes related to Knowledge Graph features (for example, Software Architecture Map UI plumbing). Take part in on-call or operational support where your team’s ownership model requires it. What you’ll bring Professional experience building and maintaining backend systems in production, with an understanding of reliability, maintainability, and how to support services over time (for example, incident response and follow-ups). Proficiency in at least one modern backend language and strong interest in Rust, with either prior Rust experience or clear evidence you can ramp quickly and deliver in a Rust-first, performance-sensitive codebase. Some exposure to distributed data or analytics systems (for example, OLAP databases, Kafka- or NATS-style messaging, or change data capture (CDC) pipelines), or strong motivation to develop those skills in this role. Interest in graph data modeling and query patterns (property graphs, multi-step (n-hop) traversals, aggregations), and willingness to learn the tools and concepts used in Knowledge Graph over time. Practical experience (or strong interest) using AI tools in day-to-day development, along with a thoughtful approach to validating outputs and integrating AI into your workflow. A language-agnostic mindset and evidence that you can pick up new languages and frameworks as needed (for example, Ruby, Go, or TypeScript/Vue where the work touches adjacent systems). Solid fundamentals in system design for your level, including the ability to reason about trade-offs, ask good questions, and align your implementation work with documented architectural decisions. Comfort working in a low-process, high-ownership environment where you take responsibility for your work, refine problem statements with teammates, and communicate progress clearly in writing across time zones in an all-remote team. About the team GitLab Orbit sits within the Data Engineering organization. We work closely with partners across AI (Duo Agent Platform), analytics, infrastructure and delivery, and security because our work spans many parts of the platform. We collaborate asynchronously rather than following a feature factory model. A key challenge for us right now is scaling sustainably as usage grows and team members take time off. That includes hardening multi-tenant behavior. At the same time, we're bringing Knowledge Graph to The base salary range for this role’s listed level is currently for residents of the United States only. This range is intended to reflect the role's base salary rate in locations throughout the US. Grade level and salary ranges are determined through interviews and a review of education, experience, knowledge, skills, abilities of the applicant, equity with other team members, alignment with market data, and geographic location. The base salary range does not include any bonuses, equity, or benefits. See more information on our benefits and equity . Sales roles are also eligible for incentive pay targeted at up to 100% of the offered base salary. United States Salary Range $115,200 — $162,000 USD How GitLab Supports Full-Time Employees Benefits to support your health, finances, and well-being Flexible Paid Time Off Team Member Resource Groups Equity Compensation & Employee Stock Purchase Plan Growth and Development Fund Parental Leave Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from underrepresented groups are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application. Country Hiring Guidelines: GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process. Privacy Policy: Please review our Recruitment Privacy Policy. Your privacy is important to us. GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also GitLab’s EEO Policy and EEO is the Law . If you have a disability or special need that requires accommodation , please let us know during the recruiting process .

Location: Remote: United States Workplace: Remote At Render, we’re building the modern cloud platform for developers creating AI-native, full-stack, multi-service applications. Our mission is to eliminate the tradeoff between the power of hyperscalers and the simplicity of developer-friendly platforms—so teams can ship fast, scale reliably, and focus on their product, not infrastructure. Unlike complex hyperscalers or ephemeral edge/serverless solutions, Render offers a developer-first experience with persistent compute, dynamic autoscaling, built-in orchestration, and observability, allowing teams to launch, scale, and manage real-world applications without writing infrastructure code or managing servers. Whether you're building LLM-powered applications, scalable SaaS products, or async processing pipelines, Render empowers teams to move fast and scale confidently from MVP to millions of users. Our platform is trusted by over 7 million developers worldwide and continues to grow rapidly. In February 2026, we raised an additional $100M in Series C financing, bringing our total funding to $260M, to accelerate our vision of making cloud infrastructure both powerful and intuitive—designed for the speed of modern AI development. We’re a diverse and talented team that values craft, velocity, and user experience. If you’re excited to help shape the future of the intelligent cloud and empower developers everywhere, we’d love to hear from you. APPLYING TO RENDER We're seeking candidates who possess high integrity, humility, and an insatiable drive to learn. Through reasoned discussions and continuous feedback, we strive to improve both individually and collectively. We foster an environment of mutual trust and respect, empowering effective debate to achieve the best outcomes for our customers and team. We especially encourage members of underrepresented groups in the tech community to apply and understand that not all successful candidates will meet each requirement listed. Our interview process is unique to each role, and we value the candidate experience just as much as our customer experience. We hope your conversations with us reflect a thoughtful process that is illuminative, enjoyable, and respectful of your time. ABOUT THE ROLE Render is looking for a Staff Developer Relations Engineer to grow usage and word of mouth for Render among developers building full-stack and AI applications. You'll be Render's technical face on stage, in workshops, at meetups, and in the developer and AI ecosystems where our users build. DX at Render has two halves. DX Engineering builds and maintains Render's tutorials, sample apps, templates, and video library. DevRel takes Render to developers in person and through the communities and partners they trust, then brings what it hears back into the product. You'll set the technical bar and direction for DevRel, and you'll be the person developers and partners go to for a deep, candid answer about running AI workloads on Render. You'll build the demos and workshop material behind your own talks. When something you build deserves a long life, DX Engineering turns it into a maintained example or template. This is a remote role within the US, with the option to work out of our headquarters in San Francisco. Expect regular travel for conferences and events. WHAT YOU'LL DO - Represent Render at conferences, meetups, hackathons, podcasts, and livestreams, with talks and live demos on building and running AI agents and full-stack apps. - Design and run hands-on workshops and hackathons, from the curriculum to the technical setup behind them. - Set DevRel's technical direction: pick the communities, ecosystems, and events worth investing in, and define what good looks like for talks, workshops, and demos. - Be the technical counterpart for ecosystem partners such as AI frameworks, agent tooling, and open source projects: joint workshops, co-hosted events, and the integrations worth building together. - Go deep in technical communities on GitHub, X, Discord, Reddit, and Hacker News. Answer hard questions, debug real deployments, and show up where Render has a credible voice. - Run the developer feedback loop. Turn what you hear in the field into clear, prioritized input for Product and Engineering, and follow it through until it ships. - Help Render's highest-potential developers and startups go further on the platform through technical deep dives and architecture reviews, and turn them into champions who talk about Render in their own communities. - Raise the bar for the DevRel team through talk reviews, demo reviews, and your own work. - Track impact through activation from events and workshops, community engagement, partner-driven adoption, and the product changes you drove. WHAT WE'RE LOOKING FOR - 8+ years in developer relations, solutions engineering, or software engineering, including at least 3 years in a developer-facing role at a developer tools, cloud, or infrastructure company. - A track record on stage: talks at recognized developer conferences, and workshops you designed and ran yourself. - Experience setting direction for a developer relations program: choosing where to invest, measuring what worked, and changing course. - Hands-on fluency with AI agents: tool calling, MCP servers, LLM APIs, and agent frameworks. You can build a working agent app live and explain what it needs to run in production, such as long-running tasks, sandboxed code execution, and retries. The field is young, so we look at what you've done recently. We don't expect years of experience in it. - The engineering depth to be credible with senior engineers. You can deploy a multi-service app, read logs, trace a failed deploy, and find the root cause in front of an audience. - Working knowledge of cloud infrastructure: containers and Docker, networking basics, CI/CD, environment configuration, and infrastructure as code. - You use coding agents like Claude Code, Codex, or Cursor every day and keep up with the agent ecosystem as it changes. - Examples of developer feedback you turned into product changes: what you heard, the case you made, and what shipped. - Excellent spoken and written communication. You can hold a room of engineers and explain a complex system clearly. NICE-TO-HAVES - Existing relationships in AI framework, agent tooling, or open source communities. - An existing developer audience through talks, livestreams, a newsletter, or social. - Experience starting or growing in-person developer meetups. - Experience with durable workflows, background job systems, or sandboxed code execution. - You've used Render before, professionally or on side projects. PROJECTS YOU MIGHT WORK ON - A workshop on deploying production agents with Render Workflows, run at a major AI conference and then at our own meetups. - A hackathon co-hosted with an AI framework partner, including the starter kit and the technical support on the day. - The talk track for an upcoming launch, delivered by you and the rest of DevRel. - A recurring field report for Product on where developers get stuck running agents on Render. If this role excites you but you don’t meet every single requirement, we’d still love to hear from you—your unique experience might be just what we need. BENEFITS - 4 weeks of paid vacation. - 14 weeks of fully paid parental leave for all parents to bond with a newly born, adopted, or fostered child. We will also work with you to create a supportive plan of return. - Long-term disability, life insurance, and 401K plans. - 100% employer-paid medical coverage and 99% employer-paid dental and vision coverage for you and a dependent. FSAs and HSAs are available as well. - Monthly lifestyle stipend for wellness, mental health and therapy, hobbies, etc. - Monthly cell phone and internet subsidy. - Commuter benefits for Renders in the Bay Area, and home office stipends for remote Renders. - Continuous learning benefits & related support. This position is generally not eligible for new visa sponsorship. At Render's discretion, the business may sponsor existing visa transfers. Applicants who require sponsorship must receive business authorization. Render is an equal-opportunity employer. We know that employing a team rich in diverse thoughts, experiences, and opinions allows our employees, product, and community to flourish. We make all employment decisions including hiring, evaluation, termination, promotional, and training opportunities, without regard to race, religion, color, sex, age, national origin, ancestry, sexual orientation, physical handicap, mental disability, medical condition, disability, gender or identity or expression, pregnancy or pregnancy-related condition, marital status, height and/or weight. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation. We encourage all who are interested to apply. We can't wait to hear from you! Render — CCPA Applicant Privacy Notice https://drive.google.com/file/d/1wKr9IejoT5McFrs9I2mahQnf6AFwb3z2/view Render — GDPR Applicant Privacy Notice https://drive.google.com/file/d/1_8jtyXQ1HdRdTdQn9sZ7n-R-zAGBmsFa/view

Location: United States Workplace: Remote ABOUT DOCKER Docker has been one of the most loved brands in developer tooling, trusted by more than 20 million monthly users and over 20 billion container image pulls. From solo founders to the world's largest companies, developers rely on Docker to build, share, and run their applications across our suite of products including Docker Desktop, Docker Hub, and Docker Scout. We are a globally distributed, remote-first team building the tools that define how software gets built and delivered. As AI agents redefine software development, Docker is at the center of that shift, providing the sandboxed environments, verified images, and secure infrastructure that make autonomous workflows trustworthy by default. _______________________________________________________________________ ABOUT THE ROLE Docker's Forward Deployed Engineering (FDE) team embeds senior engineers inside our most strategic customers to solve their hardest deployment, integration, and adoption problems. FDEs then bring what they learn back to Docker Product and Engineering so that every customer benefits. As the Principal Technical Program Manager for FDE, you define what good looks like for the organization and run the business that delivers it. You define the standards for how FDE plans, commits, delivers, and measures quality. You own the operating rhythm, track commitments Docker has made to customers, the commitments Docker Engineering has made to FDE, and the timelines and resourcing behind them. This is a senior role that works with stakeholders across the business to drive outcomes for Dockers most important customers. FDE is where Docker proves it can deliver for the customers that matter most while the technology around us changes quickly. You will set the standard for how a small team of senior engineers earns and keeps the trust of customers, and you will make what they learn useful to the whole company. The practices you establish will become the model for how Docker engages with its largest, most complex customers. _______________________________________________________________________ RESPONSIBILITIES DEFINE WHAT GOOD LOOKS LIKE - Define the FDE delivery standards, engagement model, and quality bar, and write them down so engagements are consistent across customers, regions, and engineers. - Decide which metrics matter for FDE, set targets, and revise them as the team learns. - Turn lessons from each engagement into reusable practice and product improvements. RUN THE FDE BUSINESS - Give executives a clear account of what is on track, what is at risk, what decision is needed, and from whom. - Own FDE capacity management and staffing plans with FDE leadership and recruiting: which engineers are aligned to which customers, where the gaps are, where and when to hire to stay ahead of demand. OWN CUSTOMER AND ENGINEERING COMMITMENTS - Instrument every commitment Docker has made to FDE customers, and every commitment Docker Product and Engineering teams have made to FDE. - Track commitments, map dependencies across teams, and surface risks early enough to act on them. - Negotiate and confirm dates with teams that run on their own planning processes, and hold them to those dates. INFLUENCE ACROSS DOCKER - Represent FDE with Product and Engineering leadership. Make sure customer needs are well defined using data, and that product decisions map to achievable FDEs action. - Improve the internal processes that slow collaboration between Field, Product, and Engineering, and bring those improvements to the wider company when they work. - Present FDE results and risks to Docker executives and customer stakeholders. LEAD THE PROGRAM PRACTICE - Set priorities for junior TPMs, define the reporting FDE needs, and mentor them in program management practice. - Choose the tools and standards FDE uses to track work. - May need to take part in the paid on-call rotation; respond to incidents, debug production issues, and drive systemic improvements in reliability. _______________________________________________________________________ QUALIFICATIONS REQUIRED - 10+ years of technical program management experience, including 4+ years at Staff level or above, supporting customer-facing engineering, professional services, or field engineering teams. - A track record of delivering commitments on schedule for enterprise customers, including contractual or executive-visible deadlines. - Experience defining standards, quality bars, and operating processes that other teams adopted. - Experience running dependencies across many engineering teams that you do not manage, and getting reliable dates from them through influence. - Strong technical background in cloud platforms, containers, and modern software delivery. You can read a design, challenge an estimate, and tell a real blocker from a vague one. - Experience choosing metrics and setting targets, including working with incomplete data and deciding what is good enough to act on. - Executive communication skills, with the ability to deliver difficult news early and clearly to customer stakeholders, Docker leaders, and engineers. - High agency and comfort working autonomously in a distributed, remote-first company where processes are still being defined. - Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience. - Occasional travel to customer sites and to Docker team gatherings. BONUS QUALIFICATIONS - Prior experience supporting a Forward Deployed Engineering, Professional Services, Solutions Engineering, or Customer Engineering organization. - Familiarity with the agentic AI ecosystem, including coding agents, Model Context Protocol (MCP), and enterprise requirements for security and governance of AI workloads. - Experience with regulated or compliance-driven customers in financial services, healthcare, government, or similar sectors. - Experience at a developer tools, infrastructure software, or platform company. - Familiarity with Docker products, Kubernetes, and cloud-native development platforms. WHAT SUCCESS LOOKS LIKE - FDE has written delivery standards, a quality bar, and KPI targets that FDEs, Field, Product, and Engineering all know and use. - Every customer commitment and every Docker engineering commitment to FDE has an owner and a date, and strategic customer deadlines are met. - FDE operating reviews run weekly, produce decisions, and close their actions. - FDE capacity, hiring, and customer alignment are planned ahead of demand. Visa sponsorship: Docker considers visa sponsorship on a case-by-case basis based on business needs. _______________________________________________________________________   COMPENSATION & EQUITY All full-time roles include equity. Final compensation is determined on factors including but not limited to experience, skills, qualifications, and location.   United States: $162,505 - $254,650 base ______________________________________________________________________ POSTING INFORMATION - Open vacancy: This posting is for an existing open role. - AI in hiring: Docker may use AI-assisted tools during our recruiting process. - Interview recordings: Candidates will be invited to opt in to interview recordings to support interviewer calibration and consistent evaluations. Recordings are optional and require explicit consent. ______________________________________________________________________ PERKS & BENEFITS - Remote-first by design – Work from your home, with offices in Seattle and Paris for connection and collaboration. - Flexibility that fits your life – We trust you to manage your schedule while delivering great work. - Time to recharge – Generous PTO, designated quarterly Whaleness Days, and a designated end-of-year Whaleness break. - Home office support – Set up your workspace for comfort and success. - Technology stipend – Equivalent to US$100 net per month to help support your work. - Learning & development – Annual stipend for conferences, courses, certifications, and continued learning. - Parental leave – 16 weeks of paid parental leave after six months of employment. - Equity for all full-time employees – Share in Docker's long-term success as we continue to grow. - Comprehensive benefits – Medical, retirement, and paid holidays vary by country. - Docker swag – Because representing the whale never gets old. Docker is proud to be an equal opportunity employer. We are committed to building a team that reflects a broad range of backgrounds, experiences, and perspectives. We believe diverse teams build better products, make better decisions, and better serve our global community. #LI-REMOTE

Location: United States Senior Engineering Manager, Data & AI Lead the engineering team behind Foundry, Insights, Intel, and Voice: the agents, analytics, and data intelligence of the modern freight platform. About the Role Remote, United States. Some travel is required for team onsites, customer visits, and industry events. Relocation assistance is not provided. The Senior Engineering Manager, Data & AI leads the engineering group behind Foundry, Insights, Intel, and Voice. You'll own the group's technical strategy, delivery, and quality, hire and develop its engineers, and stay hands-on by prototyping, writing evals, reading traces, and reviewing architecture in depth. The team is about eight engineers today and growing. It also owns the shared foundation those products run on: a context graph that connects loads, trips, carriers, drivers, customers, documents, and conversations; event-sourced platform data streaming into the warehouse; and the evals, tracing, guardrails, and model routing every agent depends on. Alvys facilitates more than $1 billion in funds every month across invoices, carrier payments, and driver settlements. Behind that volume is one of the richest operational datasets in freight. This team turns it into agents that do real work and intelligence customers can't get anywhere else. Engineering leaders at Alvys are builders. Expect to spend about a third of your time building. You'll set the standard for how an AI-first team works: coding agents in the daily workflow, evals before scale, and production evidence over demos. The platform runs on .NET (C#, and increasingly F#), TypeScript, and Python on Azure and AKS, with Cosmos DB, Postgres, Elasticsearch, Snowflake, dbt, and Fivetran underneath. We're looking for someone who treats AI as a first-class material, both in what we build for customers and in how the engineering team works. Industry Insight Freight runs on phone calls, emails, PDFs, and tribal knowledge. The U.S. trucking industry is worth approximately $1 trillion today and is projected to grow by roughly 60% to nearly $1.5 trillion over the next decade. Yet the work that moves that freight is still largely manual. Nearly 580,000 active U.S. motor carriers operate in a highly fragmented market; 91.5% run 10 or fewer trucks, and 99.3% run 100 or fewer. Few of these businesses will ever build a data team of their own. Every check call answered, rate confirmation read, and exception caught by software is time back for a small team running on thin margins. Freight data is also structurally messy. A single load spans brokers, carriers, drivers, shippers, and documents, each with its own identifiers, and the truth lives across EDI messages, phone calls, and free text. Generic AI tooling doesn't model any of this. That's why we're building a context graph grounded in how freight actually moves. Agents can now answer the phone, read documents, query operational data, and take actions inside a TMS. In freight, those actions move trucks and money. They must be correct, auditable, and safe. About Alvys Alvys is building the operating system for modern freight. Our AI-powered, cloud-native platform brings dispatch, driver mobile, safety and compliance, billing and settlements, native EDI, reporting, integrations, and automation into one product. We were founded in 2020 by freight industry insiders who were tired of software built by people who had never moved a load. That combination of deep customer empathy and ambitious technology still defines us. We visit customers, learn how the work actually happens, prototype quickly, debate ideas openly, and turn the best ones into products people rely on. More than 3,000 trucking and brokerage companies use Alvys today, including larger fleets with increasingly sophisticated operations. We've raised $77M to date, including a $40M Series B. That gives us real momentum and room to make bold bets, while remaining early enough that a great engineering leader can materially shape the product, team, and company. We are building more than a modern interface for a traditional TMS. Foundry, our agent platform within Alvys, helps freight teams make decisions and complete work, and this team builds it. You'll work with curious, direct, low-ego people who solve hard problems, move quickly from prototype to production, and measure customer impact. Our Principles AI makes building cheap. It does not make judgment cheap. The constraint has moved from writing code to deciding what to build and proving it is right. Agents belong in the middle of the pipeline. People remain accountable at both ends. These principles turn that into how we work every day. Own the outcome You own what you ship. "AI wrote it" is not an answer. Judgment stays human. People decide what matters and whether the result is correct. Verify independently Generation and verification are separate. Tests, review, and approval run independently of the agent that did the work. Autonomy is earned per workflow. Agents advance only on measured precision against human-verified outcomes. No number, no promotion. Design for control AI-first means security-first. Customer data stays on approved infrastructure under managed identity. Cost is an engineering constraint. Every shared workflow has termination conditions and bounded fan-out. Build durable systems Knowledge is tool-agnostic. Reviewed intent lives once; vendor-specific machinery is generated from it. Multiple vendors are a design assumption. Durable workflows don't couple to one model or harness. Twice is a pattern. Automate repeated work and share it within 48 hours. The Data & AI Portfolio The Data & AI portfolio covers Foundry, our embedded automation and agent platform, including a unified agent chat that answers questions and takes actions; Insights, conversational analytics grounded in each customer's own operating data; Intel, proprietary data intelligence drawn from third-party, licensed, and proprietary data sources; and Voice, AI voice and chat agents for load status, driver check calls, and the follow-ups in between. It also includes the shared foundation: the context graph, the data platform that feeds it, and the evals, tracing, guardrails, and model routing behind every agent. Why This Role Few engineering leadership roles combine frontier AI work, a proprietary dataset, and an industry this underserved. This one does. You build at the frontier, in production. Context graphs, self-learning agents, voice agents, and agentic automation are still being figured out across the industry. Here you ship them to real customers, on live freight operations, not benchmarks. You work with data no one else has. More than 3,000 trucking and brokerage companies run on Alvys, and more than $1 billion in funds moves through the platform every month. Combined with third-party and licensed sources, that data is the raw material for intelligence no single fleet or brokerage could build alone. Your work changes how an industry operates. With 91.5% of carriers running 10 or fewer trucks, most of our customers will never hire a data team. The agents you build give a small business the leverage of a much larger one. You own a portfolio, not a feature. Four products share one platform. You set its architecture, choose its models and vendors, and decide where the team invests as it grows. You shape the team from the start. The group is about eight engineers today. You'll hire the next wave and set the standard for how an AI-first engineering team works at Alvys. You stay close to the decisions. You report to the Head of Engineering, work directly with customers and our Forward Deployed Engineering team, and have a direct line into company planning, backed by a $40M Series B. What You'll Be Responsible For Leading and Growing the Team Manage, coach, and develop a team of about eight engineers, including senior engineers and tech leads, and grow it as the portfolio expands. Hire senior and staff engineers, and keep the quality bar high as the team scales. Run the team AI-first, with coding agents in the daily workflow and clear standards for how AI-written code is reviewed, tested, and shipped. Owning Technical Strategy Set technical direction for agents, data, and the context graph, and make the build, buy, and model calls with cost, quality, and speed in view. Make tradeoffs across four products that share one platform, including what gets built, what gets cut, and where the team invests. Building Agents That Learn Own the agent development lifecycle from prototype to production to continuous improvement. Build self-learning loops so agents improve from outcomes, user corrections, and transcripts, and make that improvement measurable. Make evals a release gate. No agent ships without tests for quality, safety, latency, and cost. Data and the Context Graph Own the context graph that links loads, trips, carriers, drivers, customers, documents, and conversations, and the entity resolution that keeps it trustworthy. Partner on the data platform that feeds it, including event streams, CDC into Snowflake, data quality, and lineage, so agents and analytics share one source of truth. Capture decision traces (the inputs gathered, the rule applied, any exception, who approved it, and why) so precedent becomes searchable for agents and people, while rules stay explicit. Combine third-party, licensed, and proprietary data sources into intelligence customers can act on, with the provenance, licensing terms, and tenant boundaries of each source respected. Safety, Isolation, and Reliability Design agents that change data with scoped permissions, human approvals, and audit trails, and defend against prompt injection and tool misuse. Enforce tenant isolation and least privilege across every agent, tool, and data path. Own reliability and unit economics. Track cost per task, per call, and per tenant, and keep margins healthy as usage grows. Staying Hands-On Spend about a third of your time building: prototype, write evals, read traces, and debug production. Review your team's designs and code with real depth, and stay current on models and tools, separating what works in production from what demos well. Cross-Functional Partnership Work as a peer to product and design leadership on direction, architecture, sequencing, and delivery. Work directly with customers and our Forward Deployed Engineering team, and turn repeated patterns into platform features. The AI Fluency We Expect We measure AI fluency by how you verify, not by how much you use AI. More autonomy is not always better. You match the level of autonomy to the risk of the work, and you hold yourself and the team to the same five competencies. Delegation: You decide whether to use AI at all, which task to hand off, and how much autonomy it gets. Description: You state goals, requirements, and limits clearly, from a single prompt up to project instructions, specs, and policy. Discernment: You check output against evidence: diffs, acceptance checks, evals, and production monitoring. Diligence: You own the effects, including data access, permissions, memory isolation, budgets, and kill switches. Systems: You know how models, context, tools, harnesses, and memory work, and where each one fails. How You Work With Agents Design multi-agent systems on paper before you build them. For each step, choose a rule, a small model, a frontier model, or a person, and measure cost per outcome, not cost per token. Start from code-orchestrated workflows and add model-led autonomy only where the task needs exploration. Keep agent-to-agent handoffs few, and give each agent a contract: inputs, allowed tools, and output schema. Put limits in code, not in prompts: hooks, sandboxed worktrees, and a stop condition, retry cap, and budget on every loop. Separate the agent that writes from the agent that checks, and give the checker fresh context. Work in an explore, plan, execute, verify, and compound rhythm, where every correction becomes a rule, skill, hook, or test. Move fluently across Claude Code, Codex, and Cursor, and choose an editor-first or agent-first surface based on who should hold the controls for the next step. Where You Take the Team Bring every engineer to running goal-based loops, where specs and checks are written first, never weakened to pass, and run by the agent itself. Move the team from steering every turn to designing the system that steers the agent: its context, its harness, and its loop. Build toward agents that run continuously in hosted sandboxes, each with its own least-privilege identity, a named owner, a budget, and a tested kill switch, with risk-scaled human review on financial actions, auth, settlements, and customer data. Close the improvement loop. Capture traces, run error analysis on real failures every week, encode the fixes, and gate every change to rules, memory, or evals behind evals and a human approver. Self-improving never means self-approving. Minimum Qualifications 8+ years building software, including 3+ years managing engineers. You've grown senior engineers and tech leads and remain a hands-on builder who can prototype, debug production, and review code credibly. Production LLM systems. You've shipped LLM-powered products to real users, not demos, and can explain what broke, what you measured, and what you changed. Agentic engineering in your own work. You build with coding agents every day and can show the multi-agent workflows, contracts, hooks, and loops you put into production, and the checks that kept them honest. Depth in agent systems. You've built with tool use, function calling, MCP, orchestration, memory, and retrieval, and you understand where agents fail. Evaluation discipline. You've built eval systems that combine offline suites, online metrics, human review, and transcript analysis, and used them to gate releases. Strong data engineering judgment. You've worked with event streams, CDC, cloud warehouses, dbt, and data quality, and you know how the shape of the data limits what models can do. Distributed systems in production. You've designed and operated production services with real reliability requirements. Multi-tenant security. You've secured multi-tenant SaaS and isolated customer data, and you treat agent permissions as a security boundary. Language fluency. You write Python or TypeScript fluently and are comfortable reading C#, F#, and .NET. Experience with F# or another functional language is a plus. Startup-stage fit. You've operated at Series B/C velocity amid ambiguity, thin support, competing priorities, and a bias toward shipping and learning. Clear technical communication. You can explain a tradeoff to an engineer, a customer, and an executive. Preferred Qualifications Knowledge graphs, context graphs, entity resolution, or GraphRAG in production. Real-time voice agents, including telephony, speech-to-text, text-to-speech, turn-taking, and latency budgets. Fine-tuning, reinforcement learning from feedback, or prompt optimization against eval sets. Embedded automation or workflow platforms such as Activepieces, n8n, or Temporal. SRE experience, or a record of running systems other teams depend on. Logistics or freight experience, or another vertical where data is deeply entangled with operations. Founder or early-stage engineering leadership experience. A history of hiring, growing, and retaining excellent engineers. Built by people who've moved freight. Come build the intelligence layer with us. Learn more at alvys.com