Вакансии

Задачи: — Владеть родительским приложением и бэкендом, включая привязку устройства, аккаунты, родительский контроль, историю активности и удаление данных. — Выпускать релизы iOS и Android с staged rollout и возможностью отката. — Выбирать и защищать технологический стек, принимать решения о распределении функций между устройством и облаком. — Руководить LLM-инженером, контролировать стоимость сессии и слой агентов в продукте. — Отвечать за техническую сторону приватности детей с правом остановить релиз по соображениям безопасности. — Проектировать команду разработки, проводить технические интервью и выстраивать инженерные практики. Требования: — Опыт full-stack разработки не менее 10 лет, с приложениями iOS и Android в продакшене. — Опыт создания детских или образовательных продуктов, включая работу с данными детей. — Минимум 3 года технического лидерства. — Ежедневное использование AI-инструментов и агентов. — Наличие выпущенных приложений с возможностью демонстрации (ссылки в сторах, демо или названия продуктов). — Владение английским языком на уровне B2 и выше. Будет плюсом: — Опыт работы с приложениями, связанными с физическими обучающими устройствами. — Знание речевых технологий (STT/TTS) и практик защиты детской приватности, например COPPA. — Опыт работы с LLM- и агентными фреймворками (LangChain, LangGraph и подобными). — Навыки работы с React Native, Expo, Next.js, GraphQL.

Location: Москва Мы в поиске  Lead Python backend-разработчика, который будет управлять командой разработчиков, развивать и внедрять процессы разработки и при этом оставаться практикующим инженером: проектировать, реализовывать и развивать микросервисы, участвовать в обсуждении архитектурных решений. Агентная разработка у нас — не строчка «будет плюсом», а то, как устроена ежедневная работа команды: кодовые агенты, реестр скиллов, правила для агентов и spec-driven development. Мы строим технологическую платформу нового поколения для производства, хранения и дистрибуции медиаконтента. Это интеллектуальная экосистема, которая автоматизирует жизненный цикл материалов: от загрузки и разметки до бесшовной публикации в десятках цифровых каналов — сайты, приложения, мессенджеры, соцсети. Обязанности ### Чем предстоит заниматься: * руководить командой backend-разработчиков: планирование, декомпозиция и оценка задач, развитие инженерных процессов * решать задачи отказоустойчивости и горизонтального масштабирования * участвовать в проектировании взаимодействия сервисов: обсуждать их ответственность, API и контракты событий * разрабатывать асинхронные микросервисы на FastAPI * реализовывать обработку событий Kafka: гарантии доставки, идемпотентность, повторную обработку; применять принятые в команде решения для согласования изменений данных и публикации событий * использовать кодовых агентов для разработки, поддерживать проектные правила и скиллы, проверять и дорабатывать сгенерированный код * работать с логами и метриками, участвовать в диагностике и устранении ошибок сервисов проекта * взаимодействовать с аналитиками, QA и frontend-разработчиками. **Стек:** Python 3.12+, asyncio, FastAPI, REST PostgreSQL, Tortoise ORM / SQLAlchemy, Redis Apache Kafka FastStream, Django / Wagtail OpenSearch / Elasticsearch GigaCode CLI, реестр скиллов, AGENTS.md / rules, MCP Docker, OpenShift (Kubernetes) Jenkins, Nexus, Bitbucket, SonarQube, Jira, Confluence Требования ### Что для нас важно: ### * от 5 лет коммерческой разработки на Python и профильное IT-образование * уверенное знание Python и asyncio: асинхронный ввод-вывод, конкурентное выполнение, понимание влияния блокирующего кода * практический опыт разработки сервисов на FastAPI, реализации REST API и участия в их проектировании * опыт микросервисной разработки, реализации взаимодействия нескольких сервисов. Понимание архитектуры сервиса * уверенный SQL и PostgreSQL: транзакции, индексы, планы запросов. Опыт работы с ORM * практический опыт с Redis: кеширование, TTL и инвалидация кеша * работа с Docker, логами и метриками приложений; опыт участия в диагностике ошибок и production-инцидентов своего сервиса * практический опыт Apache Kafka в production. Понимание гарантий доставки и порядка сообщений; реализация идемпотентной обработки и защиты от дублей * опыт руководства командой разработчиков или роли техлида: постановка задач, код-ревью, развитие людей * умение выстраивать и внедрять процессы разработки в команде. **Будет преимуществом:** * паттерны проектирования: понимание и практическое применение; умение выбирать подходящий паттерн под задачу * принципы разработки и чистый код: понимание и применение SOLID и других принципов * Kafka: диагностика consumer lag и ребалансировок, версионирование событий и совместимость схем * отказоустойчивость межсервисных взаимодействий: понимание и применение * производительность: профилирование, оптимизация и нагрузочное тестирование * фреймворки и протоколы: FastStream, Django / Wagtail, SSE, gRPC * поиск: OpenSearch / Elasticsearch; векторный поиск с pgvector / Qdrant * эксплуатация: диагностика приложений в Kubernetes / OpenShift * агентские инструменты: создание собственных скиллов, опыт настройки MCP-интеграций и разработки MCP-серверов, применение SDD в командной разработке * AI-функции продукта: интеграция LLM и разработка агентов, в том числе с LangChain / LangGraph или аналогами. Условия * комфортный современный офис по адресу Кутузовская 32 или Оружейный 41 * формат работы – офис/гибрид * ежегодный пересмотр зарплаты и годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * программа адаптации и помощь руководителя на старте * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ * подписка Прайм с возможностью совместного использования на трёх близких * вознаграждение за рекомендацию друзей в команду Сбера.

Ищем разработчика для создания AI-агентов и автоматизации рабочих процессов. — Разрабатывать и настраивать AI-агентов под задачи компании.

Проектирование и развитие масштабируемой data-инфраструктуры (batch и streaming пайплайны); ClickHouse (MergeTree), S3 Data Lake, Kafka, Airflow (ETL/ELT);

Location: Москва Мы ищем целеустремленного и амбициозного Data Scientist, который готов развиваться в области AI, ML и Финансов. Мы отвечаем за задачи по AI-трансформации, связанные с Блоком Финансы, нашим основным заказчиком является Казначейство Сбера. Нашей задачей является внедрение AI и ML в таких процессах, как продуктовое ценообразование на портфельном и индивидуальном уровне, установление единых трансфертных ставок, бизнес-планирование, с целью улучшения финансовых показателей банка. В нашу работу входит анализ данных, построение моделей, внедрение их в процесс, общение с заказчиками, представление результатов руководству Банка. В работе используем как стандартные модели и алгоритмы, так и тестируем более продвинутые методы, в том числе, совместно с Лабораторией искусственного интеллекта Сбера. Обязанности * Разработка ML-моделей и алгоритмов * Вывод в production ML-моделей и расчетных модулей * Аналитика на больших данных * Поддержка реализованных решений * Разработка ML-моделей и алгоритмов для прогнозирования временных рядов Требования * продвинутые знания в области математики (мат.анализ, линейная алгебра, теория вероятностей, мат.статистика) * python: типы данных, структуры, ООП * классический ML: задачи (классификация, регрессия), метрики, basic ML stack (pandas, numpy, sklearn, catboost, xgboost), SQL * BigData stack (Hadoop, Spark, SparkML) * умение работать с git, Jira, Confluence. Будет плюсом: * знания в области финансов * опыт продуктовой аналитики * опыт работы с временными рядами (анализ, разработка моделей) * опыт работы с LLM (langchain, langgraph) * опыт end-2-end разработки ML-моделей (EDA-MVP-Solution-Validation-Production) * опыт backend разработки. Условия * комфортный современный офис рядом с м. Кутузовская * гибридный формат работы * ежегодный пересмотр зарплаты, годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * программа адаптации и помощь руководителя на старте * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ * бесплатная подписка СберПрайм+, скидки на продукты компаний-партнеров * вознаграждение за рекомендацию друзей в команду Сбера.

Location: Warsaw, PL We bring startups and disruptive tech companies to the local market, offering engineers the opportunity to work on innovative, high-impact products. Join us and become a member of their R&D team while we take care of employment, HR support, a benefits package, and an enjoyable work environment. AITHENA is a startup focused on creating an AI-powered tool to enhance the regulatory review process in the pharmaceutical industry. Our goal is to build an AI-powered assistant for document analysis and review related to pharmaceutical regulations, enabling efficient search and user query responses based on a comprehensive database of legal case documents. ‍ We are seeking a Senior Python Engineer to build and maintain robust backend services within an existing application. ‍ Write clean, concise, and scalable Python code, ensuring stability, maintainability, and proper unit testing. Implement new requirements in an Agile environment, following architectural principles and engineering standards. Diagnose complex backend and production issues, using monitoring, telemetry, and logging tools to evaluate options and implement effective solutions. Work on performance optimization, testing, troubleshooting, and improving backend service reliability. Work with Dockerized backend services and participate in CI/CD processes using GitHub Actions. Have a basic understanding of Kubernetes, Argo CD, and Helm Charts to support troubleshooting of backend service deployments when needed. Apply basic networking and infrastructure knowledge to investigate backend service issues in Azure-based environments. Contribute to architecture and design discussions, ensuring solutions follow best practices. 5+ years of experience in development with Python. Design, develop, and maintain backend services using Python and FastAPI. Write clean, efficient, and testable code for scalable systems. Integrate and work with relational databases, primarily PostgreSQL. Implement background processing and task queues using Celery. Diagnose and resolve complex technical issues in backend systems (including cloud environments like Azure). Collaborate with the team on architecture, code reviews, and problem-solving (using GitHub for version control). Participate in performance optimization and testing (e.g., with Pytest). Apply knowledge of infrastructure and monitoring tools (e.g., Docker, Kubernetes, OpenTelemetry, Argocd) to support backend reliability. Proactively learn and adopt new technologies relevant to backend development and cloud operations. English B1+. Basic front-end experience with React Experience with LLM frameworks (LlamaIndex or LangChain) ‍ At On The Spot, we’re creating a workplace people would want to stay in: supportive, well-organized, and fair in terms of conditions and benefits. ‍ We’ll share more about our benefit package during the interviews. In the meantime, you can always check our Glassdoor reviews .

Location: San Francisco, CA Workplace: OnSite ABOUT US At LangChain, our mission is to enable every company to own its intelligence. We believe agents will transform how companies operate, and we're building the foundation to make that future possible. Our open-source harnesses and LangSmith platform give developers and enterprises the infrastructure to run, improve, and govern agents at scale. We've raised $460M to date, with our most recent funding round valuing the company at $3B, backed by leading investors including Sequoia, Benchmark, IVP, and CapitalG. As we continue to expand our platform and tackle some of the hardest challenges in agent engineering, every team member has the opportunity to make a meaningful impact and help shape both our technology and the future of the industry. More than 7,000 organizations rely on LangSmith, from fast-growing startups to Fortune 500 enterprises, including NVIDIA, Bridgewater, LinkedIn, Workday, and Rippling. We're just getting started on making high-performing agents easier to build and manage at scale. ABOUT THE ROLE We're hiring an experienced Performance Marketer to own and scale paid search and emerging AI advertising channels at LangChain, starting with Google Ads, Bing (Microsoft Advertising), and ChatGPT. You'll own the entire paid acquisition experience, from developing channel strategy and building campaigns to optimizing landing pages, improving conversion rates, and driving qualified pipeline. This is a highly hands-on role. You'll be the person building campaigns, analyzing performance, testing creative, improving landing pages, and identifying new opportunities to scale. We're looking for someone who combines deep paid search expertise with a willingness to experiment with emerging advertising platforms. You should be comfortable managing significant budgets, making data-driven investment decisions, and connecting advertising performance to downstream pipeline rather than just clicks, leads, or conversions. WHAT YOU'LL OWN PAID ACQUISITION STRATEGY AND PIPELINE - Own the strategy across Google Ads, Bing, and ChatGPT advertising, with the opportunity to expand into additional AI-native advertising channels. - Develop channel-specific strategies based on audience intent, available targeting capabilities, and expected pipeline contribution. - Own budget allocation, forecasting, and performance against pipeline targets. - Balance investment across branded, non-branded, competitor, and product-specific campaigns, along with emerging AI advertising opportunities. - Identify opportunities to scale high-performing campaigns and build repeatable playbooks for new channels. - Develop testing roadmaps across audiences, messaging, bidding, conversion experiences, and new advertising formats. END-TO-END CAMPAIGN EXECUTION You'll own campaign execution across all three channels: - Google Ads: Build, launch, and manage campaigns across branded, non-branded, competitor, and product search, including keyword research, match types, negative keywords, bidding, and budget optimization. - Bing / Microsoft Advertising: Develop and manage campaigns tailored to Microsoft's search audience, identifying opportunities to extend reach and capture incremental high-intent demand. - ChatGPT advertising: Develop and execute an early advertising strategy, testing available placements, audience approaches, messaging, and conversion experiences as the platform evolves. - Creative and messaging: Write and test ad copy in partnership with Product Marketing, adapting messaging to each channel and its user intent. - Experimentation: Run structured tests to improve acquisition efficiency, traffic quality, and pipeline conversion. - Campaign operations: Own campaign setup, tracking validation, launch readiness, troubleshooting, and day-to-day optimization. PAID CONVERSION EXPERIENCE AND WEBSITE OPTIMIZATION - Own the website conversion experience for traffic acquired through Google, Bing, and ChatGPT. - Build and optimize dedicated landing pages for paid acquisition campaigns using Webflow or similar tools. - Develop and execute A/B tests across landing page messaging, layouts, CTAs, and forms. - Identify friction across the full user journey, from ad interaction to product sign-up, demo request, or meeting booking. - Optimize conversion paths for both developers exploring LangSmith and enterprise buyers evaluating commercial solutions. - Partner with Marketing Operations to improve form conversion, qualification, routing, and meeting booking. - Ensure landing page experiences reflect the user's intent and context across different advertising channels. MEASUREMENT, REPORTING, AND OPTIMIZATION - Own performance reporting across spend, traffic, conversions, qualified leads, meetings, opportunities, and pipeline. - Develop channel-specific measurement frameworks for Google Ads, Bing, and ChatGPT based on their available reporting and conversion capabilities. - Partner with Marketing Operations to connect advertising conversion signals with HubSpot and Salesforce data. - Optimize established search channels toward downstream pipeline signals, including offline conversion tracking where supported. - Establish a testing and measurement approach for emerging channels where attribution and optimization capabilities may be less mature. - Analyze performance across channels, audiences, campaigns, and landing pages to identify where to invest, optimize, or reduce spend. CROSS-FUNCTIONAL PARTNERSHIP - Partner with Product Marketing to translate positioning into search campaigns, AI advertising messaging, and landing page experiences. - Work closely with Web and Design on landing page development and conversion experimentation. - Collaborate with Marketing Operations on tracking, attribution, CRM integrations, and reporting accuracy. - Align with Demand Generation on product launches, campaign priorities, budget allocation, and pipeline targets. WHAT WE'RE LOOKING FOR - 5–11 years in performance marketing, paid search, or B2B growth marketing, ideally in developer tools, infrastructure, or technical SaaS. - Deep hands-on expertise in Google Ads and experience managing Microsoft Advertising. - Proven experience personally building, launching, and optimizing paid search campaigns. - Strong understanding of campaign architecture, keyword intent, bidding strategies, negative keywords, and search budget optimization. - Experience testing new advertising platforms or emerging acquisition channels. Curiosity about AI-native search and advertising is important. - Strong experience with conversion rate optimization, landing page development, and A/B testing. - Experience managing meaningful budgets and optimizing toward qualified pipeline and revenue outcomes. - Strong analytical skills and fluency in CPC, CVR, CPL, cost per opportunity, pipeline contribution, and attribution. - Working knowledge of Google Ads, Microsoft Advertising, GA4, Google Tag Manager, HubSpot, Salesforce, and Flint/Webflow. - Comfortable with technical products and developer audiences. - A strong experimentation mindset and willingness to build new programs without an established playbook. - Comfortable with ambiguity and true ownership. COMPENSATION We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, and life insurance. Salary Range: $165,000-$190,000 COMPENSATION PHILOSOPHY: We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. We are hiring for this role across a range of scope, and the compensation band reflects that. Your offer depends on what we learn about your impact, craft, leadership, and communication during the interview process. We will tell you early where you’re tracking and talk comp with you before offer stage. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations. BENEFITS Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.

Location: San Francisco, CA Workplace: OnSite ABOUT US At LangChain, our mission is to enable every company to own its intelligence. We believe agents will transform how companies operate, and we're building the foundation to make that future possible. Our open-source harnesses and LangSmith platform give developers and enterprises the infrastructure to run, improve, and govern agents at scale. We've raised $460M to date, with our most recent funding round valuing the company at $3B, backed by leading investors including Sequoia, Benchmark, IVP, and CapitalG. As we continue to expand our platform and tackle some of the hardest challenges in agent engineering, every team member has the opportunity to make a meaningful impact and help shape both our technology and the future of the industry. More than 7,000 organizations rely on LangSmith, from fast-growing startups to Fortune 500 enterprises, including NVIDIA, Bridgewater, LinkedIn, Workday, and Rippling. We're just getting started on making high-performing agents easier to build and manage at scale. ABOUT THE ROLE We're hiring an Integrated Campaigns Manager to own how LangChain brings our biggest narratives to market through integrated, multi-channel campaigns and webinars programs that generate awareness, engagement, and pipeline globally. You'll partner closely with Product Marketing, Solutions, Industry Marketing, and Thought Leadership to translate messaging, customer stories, and content into full-funnel campaigns. From defining the campaign strategy and channel mix to building landing pages, launching webinars, coordinating promotion, and optimizing conversions, you'll own execution from start to finish. You'll be responsible for connecting the dots across channels, audiences, and regions, ensuring we're not just launching individual tactics but building cohesive campaigns that move prospects through the funnel. We're looking for someone who is equally comfortable developing a campaign strategy and getting into the details of execution. You should know how to turn an idea into a live campaign, measure what's working, and continuously optimize to drive pipeline. WHAT YOU'LL OWN GLOBAL INTEGRATED CAMPAIGN STRATEGY AND PIPELINE - Build and own global integrated campaign plans across our core product, solutions, industry, and thought leadership narratives. - Partner with Product Marketing, Solutions, Industry Marketing, and Thought Leadership to translate positioning and content into campaigns with clear audiences, messaging, channels, and conversion goals. - Develop full-funnel campaign strategies that connect awareness and education to demand capture and pipeline generation. - Define campaign goals, audience segmentation, channel investments, and success metrics, with accountability for campaign-sourced and influenced pipeline. - Partner with regional demand generation and field marketing teams to adapt global campaigns for NAMER, EMEA, and APAC markets. - Build repeatable campaign playbooks and identify opportunities to scale successful programs across regions. END-TO-END CAMPAIGN EXECUTION You'll own the campaign from initial concept through launch, optimization, and reporting, and be hands-on throughout. - Integrated campaigns: Build and execute multi-channel campaigns across email, paid media, organic social, webinars, third-party promotion, and sales outreach. - Webinars and virtual programs: Own end-to-end webinar execution, including programming strategy, speaker coordination, promotional planning, registration and attendance optimization, and post-event follow-up. Programs may include thought leadership webinars, product education, live demos, and industry-specific sessions. - Campaign assets and landing pages: Partner with content, design, and web teams to develop campaign assets and landing pages, ensuring consistent messaging and compelling conversion paths. - Lifecycle: Partner with lifecycle marketing to develop segmented nurture programs, audience journeys, and follow-up campaigns that move prospects from initial engagement toward sales opportunities. - Paid and digital: Partner with performance marketing on audience targeting, paid promotion, retargeting, campaign creative, and conversion strategies. - Sales activation: Build campaign enablement and outreach plays with sales and SDR teams to turn engagement into meetings and opportunities. CONVERSION OPTIMIZATION AND CAMPAIGN PERFORMANCE - Own campaign conversion performance across landing pages, webinar registrations, content downloads, and other campaign conversion points. - Identify friction across the customer journey and develop experiments to improve conversion rates and lead quality. - Optimize campaign messaging, creative, targeting, offers, and CTAs based on performance. - Track campaign effectiveness across engagement, MQLs, meetings, opportunities, and pipeline. - Partner with Marketing Ops to ensure proper campaign tracking, attribution, and reporting. - Translate performance insights into recommendations on where to invest, what to improve, and what to scale. CROSS-FUNCTIONAL PARTNERSHIP - Work directly with Product Marketing and content marketing to understand product positioning, launches, and messaging priorities and translate them into global campaign strategies. - Identify compelling narratives, customer proof points, and campaign opportunities with cross-functional partners. - Partner with regional and field marketing teams to ensure global campaigns are relevant to local audiences and sales priorities. - Work closely with Performance Marketing, Lifecycle, and Marketing Ops to build coordinated programs across channels. - Collaborate with Sales and SDR teams on campaign activation, account targeting, and follow-up strategies that drive pipeline. - Own campaign planning, timelines, stakeholder alignment, and execution to ensure campaigns launch successfully. WHAT WE'RE LOOKING FOR - 5–11+ years of experience in B2B demand generation, integrated campaigns, or growth marketing, ideally in developer tools, infrastructure, AI, or technical SaaS. - Proven experience building and executing multi-channel integrated campaigns, with measurable pipeline or revenue outcomes. - You've personally built and launched campaigns, including landing pages, email programs, webinars, promotional plans, and nurture journeys. - Strong understanding of full-funnel campaign strategy, audience segmentation, channel orchestration, and conversion optimization. - Experience running end-to-end webinar and virtual event programs, from planning and promotion to follow-up and performance analysis. - You're comfortable translating technical product messaging and thought leadership into campaigns that resonate with both technical practitioners and enterprise decision-makers. - Strong analytical skills and fluency in funnel conversion, MQLs, cost per opportunity, campaign attribution, and pipeline contribution. - Experience working across global and regional marketing teams, balancing consistent campaign messaging with local market needs. - Working knowledge of modern marketing tools such as HubSpot, Salesforce, Goldcast, Zoom Webinar, and Google Analytics. - Strong project management skills with the ability to coordinate multiple stakeholders, prioritize competing initiatives, and launch campaigns on schedule. - You're a builder. You can develop the strategy, write the copy, build the landing page, launch the campaign, pull the report, and optimize based on the results. - Comfortable with ambiguity, fast-moving environments, and true end-to-end ownership. COMPENSATION We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, and life insurance. Actual compensation and offerings will vary based on role, level, and location. Salary Range: $165,000-$190,000 COMPENSATION PHILOSOPHY: We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. We are hiring for this role across a range of scope, and the compensation band reflects that. Your offer depends on what we learn about your impact, craft, leadership, and communication during the interview process. We will tell you early where you’re tracking and talk comp with you before offer stage. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations. BENEFITS Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.

Задачи: вести команду разработки в области Agentic AI с использованием платформы V Synapse Service Mesh. Требования: опыт работы с Golang, Kubernetes, микросервисами, искусственным интеллектом, машинным обучением, генеративным ИИ, LangChain и LangGraph. Условия: гибридный формат работы. Компания: Сбер Банк — крупнейший российский финансовый конгломерат, предоставляющий широкий спектр банковских услуг для частных лиц, малого бизнеса и корпоративных клиентов.

Location: Canada What The Role Is We're hiring a Staff MLE for the Discovery team to own recommendations and personalization across Babylist's consumer experience — the homepage feed, product recs, search, and the ML-powered systems that make registry building feel effortless. Babylist was built on editorial recommendations: products chosen by people with deep baby gear expertise. That editorial foundation is a big part of why millions of families trust us. We're now building ML-powered personalization on top of it, using one of the richest first-party datasets in parenting. We're early in this work, and we have a real mandate. A small Discovery team has initial retrieval and reranking models live on parts of the site and a steady cadence of A/B tests. We are looking for a Staff MLE who has seen personalization done well at scale and can set the technical direction for where we go next. Registry building is the heart of the Babylist product. Every parent builds a list of dozens of products, from swaddle to stroller, with real stakes (a friend or family member is going to buy these things, and a baby is going to use them). As a universal registry, this registry also typically spans many retailers. This makes registry building on Babylist one of the most interesting personalization problems in consumer e-commerce: latent intent, life-stage progression, multi-stakeholder gift dynamics, deep declarative signal in millions of completed registries, cross-retailer datapoints, and a user who genuinely wants help. If you want to join a mature ML org and tune models at the margins, this isn't the right role. If you've worked inside a strong recommendations team, learned what good looks like, and want to build from zero-to-one at a company earlier in the journey, read on. What You'll Own You'll be the technical lead for personalization on the Discovery team, working alongside a PM, Engineering Manager, Senior MLE and fullstack software engineers. You set where our models go over the next year or two, sequence the bets that get there, and stay deep in building. A few examples of the problems you’ll get to shape: Product recommendations across every surface. Product detail pages, add-next recommendations after someone adds to their registry, vibes/aesthetic representations of products and the shared models underneath them. Personalizing the Feed. Deciding which products, editorial content and social content each family sees, and in what order. Personalizing search. Bringing what we know about a family into search results, and helping decide how much search should share retrieval and ranking with recommendations. Making Addie (our AI registry assistant) better at recs. Working with the team behind Addie so its product suggestions draw on the same models and signals as the rest of the site. In practice, you will: Take a fuzzy business problem from first sketch to production model, and own whether it moved the metric. Make the modeling and architecture calls that span surfaces and are expensive to reverse. Design the evaluation, offline and online, that tells us a model is good before and after it ships. Know when a rule or a simple baseline is the right first step, and when it's time to pursue more advanced approaches. Partner with product, design and data to help shape what's worth building. Coach engineers on the team, and more broadly be an ML expert and resource to the wider engineering org. Who You Are You’ve shipped recommendation and/or personalization systems that reached real users at scale within a consumer product, and can point to the business impact of your work. You’ve done this within a team that did ML well — you know what good looks like, and are motivated to bring that to a company earlier in their ML journey. You bring: Expertise in recommender fundamentals: for example, candidate generation vs. ranking, offline vs. online evaluation, cold start solutions, and handling position and popularity bias. Fluency in the Python ML stack and comfort owning models through deployment and monitoring, including contributions to our application backend. An ability to take an ambiguous problem and start moving before anyone hands you the full picture. A builder's instinct for early-stage ML. You understand effort vs. effectiveness tradeoffs and can advocate for when a rule beats a model, or a model is “good enough” for early learnings. An outcome and user focused mindset. You measure yourself by business impact, and you can connect a model change to user experience & registry adds, conversion or revenue. AI-native daily practice. You're already using AI to move faster and improve your output, and you stay curious about what's coming next. Compensation We post real numbers. For a Canada-based Staff Engineer, the starting base salary range is 299,300 to 372,600 CAD plus a target annual bonus of 20 percent of base. That's total target cash of roughly 359,160 to 447,120 CAD. On top of that you get meaningful equity and a 401(k) match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope. How We Build AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome. The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers. The Stack Core app: Rails, Packwerk, React, TypeScript, Sidekiq Mobile: iOS (Swift), Android (Kotlin) Data, search & events: MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma Machine learning: deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow AI & dev tooling: Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim Infra & ops: AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly Key integrations: Shopify (payments), Iterable (CRM) Why Babylist An engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done. Ten million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide. How We Work Remote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis. How We Hire Three rounds, usually two to three weeks start to finish. Recruiter conversation (30 minutes). Trade context: what you want next, what we're building and straight answers on comp, team and remote. Technical screen (1 hour). One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles. Final round (4 hours). Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours. If your timeline is tight, tell us and we'll move faster. Benefits Company-paid medical and fully covered dental and vision A 401(k) match Generous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program Winter Wonder Week, a paid company-wide week off at the end of the year A remote-work stipend Mental-health and wellness support A Few Things To Know We record and transcribe interviews to evaluate candidates, in line with applicable privacy laws. We expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking. If you have a family member or close relationship with a Babylist employee, let your recruiter know. Official outreach only ever comes from an @babylist.com address

Location: United States What The Role Is We're hiring a Staff MLE for the Discovery team to own recommendations and personalization across Babylist's consumer experience — the homepage feed, product recs, search, and the ML-powered systems that make registry building feel effortless. Babylist was built on editorial recommendations: products chosen by people with deep baby gear expertise. That editorial foundation is a big part of why millions of families trust us. We're now building ML-powered personalization on top of it, using one of the richest first-party datasets in parenting. We're early in this work, and we have a real mandate. A small Discovery team has initial retrieval and reranking models live on parts of the site and a steady cadence of A/B tests. We are looking for a Staff MLE who has seen personalization done well at scale and can set the technical direction for where we go next. Registry building is the heart of the Babylist product. Every parent builds a list of dozens of products, from swaddle to stroller, with real stakes (a friend or family member is going to buy these things, and a baby is going to use them). As a universal registry, this registry also typically spans many retailers. This makes registry building on Babylist one of the most interesting personalization problems in consumer e-commerce: latent intent, life-stage progression, multi-stakeholder gift dynamics, deep declarative signal in millions of completed registries, cross-retailer datapoints, and a user who genuinely wants help. If you want to join a mature ML org and tune models at the margins, this isn't the right role. If you've worked inside a strong recommendations team, learned what good looks like, and want to build from zero-to-one at a company earlier in the journey, read on. What You'll Own You'll be the technical lead for personalization on the Discovery team, working alongside a PM, Engineering Manager, Senior MLE and fullstack software engineers. You set where our models go over the next year or two, sequence the bets that get there, and stay deep in building. A few examples of the problems you’ll get to shape: Product recommendations across every surface. Product detail pages, add-next recommendations after someone adds to their registry, vibes/aesthetic representations of products and the shared models underneath them. Personalizing the Feed. Deciding which products, editorial content and social content each family sees, and in what order. Personalizing search. Bringing what we know about a family into search results, and helping decide how much search should share retrieval and ranking with recommendations. Making Addie (our AI registry assistant) better at recs. Working with the team behind Addie so its product suggestions draw on the same models and signals as the rest of the site. In practice, you will: Take a fuzzy business problem from first sketch to production model, and own whether it moved the metric. Make the modeling and architecture calls that span surfaces and are expensive to reverse. Design the evaluation, offline and online, that tells us a model is good before and after it ships. Know when a rule or a simple baseline is the right first step, and when it's time to pursue more advanced approaches. Partner with product, design and data to help shape what's worth building. Coach engineers on the team, and more broadly be an ML expert and resource to the wider engineering org. Who You Are You’ve shipped recommendation and/or personalization systems that reached real users at scale within a consumer product, and can point to the business impact of your work. You’ve done this within a team that did ML well — you know what good looks like, and are motivated to bring that to a company earlier in their ML journey. You bring: Expertise in recommender fundamentals: for example, candidate generation vs. ranking, offline vs. online evaluation, cold start solutions, and handling position and popularity bias. Fluency in the Python ML stack and comfort owning models through deployment and monitoring, including contributions to our application backend. An ability to take an ambiguous problem and start moving before anyone hands you the full picture. A builder's instinct for early-stage ML. You understand effort vs. effectiveness tradeoffs and can advocate for when a rule beats a model, or a model is “good enough” for early learnings. An outcome and user focused mindset. You measure yourself by business impact, and you can connect a model change to user experience & registry adds, conversion or revenue. AI-native daily practice. You're already using AI to move faster and improve your output, and you stay curious about what's coming next. Compensation We post real numbers. For a US-based Staff Engineer, the starting base salary range is $233,500 to $290,700, plus a target annual bonus of 20 percent of base. That's total target cash of roughly $280,200 to $348,840. On top of that you get meaningful equity and a 401(k) match. Where you start in that range depends on your experience, and your pay grows from there with performance and scope. How We Build AI is the default here. Engineers run agentic sessions for most of the work, and a lot of the interesting engineering now lives in the scaffolding that makes the agents good: the eval harnesses, the curated context, custom review skills and fast CI. Agents also triage incidents and handle a big share of support. A human always owns the outcome. The architecture is intentionally simple: one Rails monolith, MySQL and few moving parts. That's deliberate. Simple infrastructure lets us move fast and lets AI reason about the whole system, so the hardest problems are the ones in front of customers. The Stack Core app: Rails, Packwerk, React, TypeScript, Sidekiq Mobile: iOS (Swift), Android (Kotlin) Data, search & events: MySQL, Snowflake, dbt, Airflow, Weaviate, WarpStream, Hex, Sigma Machine learning: deep learning, matrix factorization, retrieval & ranking, AWS SageMaker, MLflow AI & dev tooling: Claude Code, Devin, Linear, CodeRabbit, Warden, LangChain, Bedrock AgentCore, Maxim Infra & ops: AWS, Kubernetes, Terraform, GitHub Actions, Datadog, incident.io, LaunchDarkly Key integrations: Shopify (payments), Iterable (CRM) Why Babylist An engineer, expecting her first baby, couldn't find the registry she wanted. So she built it. That's how Babylist started, and it's still how we work: engineers solving problems for families. Becoming a parent is one of the biggest moments in a person's life. Millions reach it for the first time every year, making thousands of decisions and figuring it out as they go. That's who we build for, and we're a long way from done. Ten million people give gifts through Babylist every year. We did more than $750M in revenue in 2025, up 45 percent over the year before, and we've been profitable for eight years while staying independent. So you can take on a hard, multi-year problem without watching over your shoulder for the next round or the next correction. And the team is small, around 65 engineers, so what you ship stays visible and your scope stays wide. How We Work Remote-first across the US and Canada, and we have been for years. That's not changing. We trust you to own your time and your outcomes, and we get everyone in a room together twice a year. Teams are small, pods of three to five engineers, so nothing you ship disappears into a committee. You'll work shoulder to shoulder with product, design and data, and with the partners across the business who rely on what you ship. You'll also stay close to customers yourself: sitting in on user interviews, watching session recordings, riding along with support. Here that's part of the engineering job, on a regular basis. How We Hire Three rounds, usually two to three weeks start to finish. Recruiter conversation (30 minutes). Trade context: what you want next, what we're building and straight answers on comp, team and remote. Technical screen (1 hour). One round, no AI, language-agnostic. Reach for Google when you normally would. We just want to see how you reason from first principles. Final round (4 hours). Four one-hour interviews: system design, AI-assisted coding with the tools you'd actually use here, product sense and culture and values. You'll hear back either way within 24 to 48 hours. If your timeline is tight, tell us and we'll move faster. Benefits Company-paid medical and fully covered dental and vision A 401(k) match Generous paid parental leave for birthing and non-birthing parents, plus a gradual return-to-work program Winter Wonder Week, a paid company-wide week off at the end of the year A remote-work stipend Mental-health and wellness support A Few Things To Know We record and transcribe interviews to evaluate candidates, in line with applicable privacy laws. We expect you to use AI in your work and we welcome it in the process, but what you submit and say should reflect your own thinking. If you have a family member or close relationship with a Babylist employee, let your recruiter know. Official outreach only ever comes from an @babylist.com address

Location: London Workplace: OnSite ABOUT US At LangChain, our mission is to enable every company to own its intelligence. We believe agents will transform how companies operate, and we're building the foundation to make that future possible. Our open-source harnesses and LangSmith platform give developers and enterprises the infrastructure to run, improve, and govern agents at scale. We've raised $460M to date, with our most recent funding round valuing the company at $3B, backed by leading investors including Sequoia, Benchmark, IVP, and CapitalG. As we continue to expand our platform and tackle some of the hardest challenges in agent engineering, every team member has the opportunity to make a meaningful impact and help shape both our technology and the future of the industry. More than 7,000 organizations rely on LangSmith, from fast-growing startups to Fortune 500 enterprises, including NVIDIA, Bridgewater, LinkedIn, Workday, and Rippling. We're just getting started on making high-performing agents easier to build and manage at scale. ABOUT THE ROLE We're hiring a Vice President to own and lead our EMEA business. You'll inherit a team of 20+ quota-carrying Commercial and Enterprise AE's across four hubs: London, Amsterdam, Munich, Stockholm and be responsible for building the management layer, operating model and partner ecosystem that takes the EMEA region from early traction to consistent, scaled revenue. This is a second-line leader role that will see you report directly to the Global Vice President of Sales and operate with meaningful autonomy across pipeline, hiring, partner relationships and executive engagement. WHAT YOU'LL OWN Team leadership and performance - Lead, develop and expand the Sales teams across EMEA from day one - Run weekly pipeline reviews, deal coaching sessions, and forecast calls with precision and consistency - Identify and close performance gaps early: coach the team to close larger deals faster and navigate complex, multi-stakeholder cycles - Build a culture of accountability and development; create clear career paths for senior individual contributors, including first-line management opportunities as the team scales Revenue - Own the EMEA number: quarterly and annual quota attainment across all sub-regions - Build and maintain a healthy pipeline that supports consistent delivery and predictable forecasting to leadership - Engage directly in strategic deals as executive sponsor, bringing credibility and commercial weight to enterprise negotiations - Establish the region's strongest lighthouse accounts across key industry verticals, building referenceable wins that anchor LangChain's market position in EMEA Hiring - Manage multiple open roles across EMEA simultaneously: owning the full hiring lifecycle across pipeline, assessment and closing - Partner with the internal talent team to move at pace without compromising on quality - Define the headcount profile for EMEA beyond Q4 and feed into H1 planning, ensuring the team is built ahead of the revenue curve Partner ecosystem - Owns hyperscaler and GSI/RSI relationships (AWS, Google Cloud, Azure, and key SIs) across EMEA - Develop a structured partner motion that generates qualified pipeline and supports deal acceleration, reduces risk and drives expansion - Represent LangChain at partner QBRs and executive engagements in region Market presence - Represent LangChain as a keynote-ready leader at partner events, customer engagements, and industry conferences across the region: including KubeCon, Money20/20, Nordic AI summits, and customer EBCs - Build LangChain's brand with technical buyers, CIOs, and innovation leaders across Northern Europe's most important enterprise accounts - Bring intelligence on the EMEA markets back to the Global VP of Sales and product teams to inform roadmap and go-to-market positioning WHAT WE'RE LOOKING FOR Experience - 10+ years in enterprise SaaS sales, with at least 5+ years in a regional or sub-regional leadership role managing quota-carrying AE's - Demonstrable track record of hitting and exceeding regional revenue targets across multiple quarters - Experience managing teams across multiple countries and time zones, with the cultural intelligence to operate across EMEA - Background selling to technical buyers (engineering leaders, CTOs, platform teams): you understand how developer tools get evaluated and adopted - Experience in the AI, data, or developer tooling space strongly preferred; familiarity with the LangChain ecosystem is a meaningful plus Leadership - You've run teams of 20+ AEs before and know the difference between managing and coaching - You can hold a forecast conversation with a VP and a deal coaching session with an AE in the same afternoon and add value in both - You hire well and fast. You know what good looks like for enterprise AE profiles and you can run structured hiring processes across four open roles simultaneously - You've built or contributed to a partner motion before: you understand what "partner pipeline" means in practice, not just in theory Operating style - You bias toward action. You don't wait for a playbook to be handed to you: you build it - You're comfortable with ambiguity and early-stage scale. This is not a role with a fully defined motion; you'll shape a significant part of it - You communicate clearly and directly, to your team and to leadership - You're present in the market. You attend the right events, know the right people and build relationships that translate into pipeline and retention COMPENSATION PHILOSOPHY: We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. We are hiring for this role across a range of scope, and the compensation band reflects that. Your offer depends on what we learn about your impact, craft, leadership, and communication during the interview process. We will tell you early where you’re tracking and talk comp with you before offer stage. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations. BENEFITS Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.

Location: United States (Remote) Grafana Labs is the company behind Grafana Cloud, the fully managed observability platform trusted by more than 10,000 organizations to ensure reliability, resolve incidents faster, and optimize telemetry at scale. Built on open source and open standards and designed for interoperability across any stack, Grafana Cloud brings AI to observability and observability to AI, giving teams (and their agents) unified visibility so they can see, understand, and act on all their disparate data, wherever it lives, and move at the speed of their ambitions. Customers, including Anthropic, Bloomberg, NVIDIA, Microsoft, and Salesforce, rely on Grafana Labs. We are a 100% remote company with team members across 40+ countries, backed by leading investors including Lightspeed Venture Partners, Sequoia Capital, GIC, Coatue, J.P. Morgan, CapitalG, and Lead Edge Capital. Learn more at grafana.com and follow us on LinkedIn and X . We’re scaling fast and staying true to what makes us different: an open-source legacy, a global collaborative culture, and a passion for meaningful work. Our team thrives in an innovation-driven environment where transparency, autonomy, and trust fuel everything we do. You may not meet every requirement, and that’s okay. If this role excites you, we’d love you to raise your hand for what could be a truly career-defining opportunity. This is a remote opportunity and we are looking for candidates from the U.S. The Opportunity Grafana Labs is seeking a Staff Engineer (AI & Automation) to own the AI agent infrastructure and automation platform that powers our Marketing Operations organization. You’ll build multi-agent architectures, LLM integrations, and backend services that connect AI models to internal and third-party data platforms. You’ll ship production systems that teams depend on daily. This is a high-autonomy role where you own the technical direction. You’ll identify the highest-leverage problems across Marketing, RevOps, and SDR teams, design the solutions, and ship them. You’ll define the technical direction for the automation platform (data models, API contracts, shared libraries, reference architectures) and partner with Data Engineering, GTM Systems, and Field Operations to build scalable, self-service automation that eliminates manual work and drives operational efficiency. What You'll Be Doing Agentic Systems & AI Infrastructure Own end-to-end development of multi-agent AI systems, from architecture and implementation through testing, deployment, and ongoing operation Build modular, composable agentic systems using orchestration frameworks (LangChain, CrewAI, Anthropic MCP, or similar) that operate 24/7 across teams Develop reusable agentic skills that agents invoke across interfaces (Slack, dashboards, internal apps, CLIs) Implement observability and feedback loops including logging, performance metrics, prompt iteration, model evaluation, and cost management Establish governance and compliance standards for AI workflows including access controls, audit trails, PII handling, and human-in-the-loop escalation paths Systems Integration & Backend Services Build MCP servers, APIs, CLIs, and microservices connecting AI models to business systems (BigQuery, Slack, CRMs, email, calendars, analytics tools) Architect data flows for retrieval-augmented generation (RAG), connecting LLMs to internal knowledge bases, customer data, and real-time business context Build serverless or containerized services (GCP Cloud Functions, Cloud Run) that scale with usage and integrate with Grafana's cloud infrastructure Automation & Workflow Enablement Partner with RevOps, Demand Generation, Regional Marketing, and SDR teams to scope high-impact automation problems, identify bottlenecks, and build solutions with measurable business outcomes Design and deploy workflows using orchestration tools (n8n, Workato, or custom platforms) with CI/CD, testing, and production reliability standards Build systems designed for self-service with documentation, playbooks, and enablement materials that let partner teams operate independently We invest heavily in developer productivity. You'll have access to AI coding assistants (Claude Code, Gemini CLI, OpenAI Codex, and others of your choice within security guidelines). We encourage pragmatic AI-assisted development paired with strong code review and quality standards. What Makes You a Great Fit 8+ years of software engineering experience with depth in backend development, systems integration, or data/analytics engineering 2+ years hands-on experience applying LLMs/AI to production workflows, not just prototypes Strong proficiency in Python and JavaScript/Node.js with Git-based workflows, code review practices, and testing discipline Hands-on experience with LLM frameworks and patterns including prompt engineering, RAG, function calling/tool use, structured output parsing, and evaluation Experience building and operating multi-agent systems at scale including agent decomposition, orchestration patterns (sequential chains, router/dispatcher, parallel fan-out), state management, and production monitoring You diagnose business problems before writing code. You think in workflows and outcomes, not just functions. Deep familiarity with Google Cloud Platform, BigQuery, and serverless/containerized services (Cloud Functions, Cloud Run) Understanding of LLM failure modes and production mitigations including confidence thresholds, fallback logic, human escalation, and cost/latency management Proven ability to identify high-leverage problems, push back on low-impact requests, and deliver end-to-end with minimal direction Fluent with AI-assisted development tools (GitHub Copilot, Cursor, Claude Code). You use AI to build AI systems Clear technical communicator who can explain complex systems in simple terms to both engineers and business stakeholders Bonus Points Experience with vector databases or retrieval pipelines (Pinecone, Weaviate, ChromaDB, Qdrant, pgvector) Familiarity with marketing or sales platforms (Salesforce, Customer.io, HubSpot, Marketo, Outreach) Experience with frontend frameworks (React, Slack Block Kit) for building user-facing AI tool interfaces Observability tooling for AI systems (LangSmith, Weights & Biases, custom evaluation frameworks) Experience with workflow orchestration platforms (n8n, Temporal, Prefect, Airflow) Familiarity with Model Context Protocol (MCP) or similar standards for connecting AI systems to data sources Prior work automating marketing, sales, or customer success workflows in a B2B SaaS environment Active in open-source communities. Grafana is built on OSS and we value engineers who share that DNA In the United States, the base compensation range for this role is USD $154,445 - USD $185,334. Actual compensation may vary based on level, experience, and skillset as assessed throughout the interview process. All of our roles include Restricted Stock Units (RSUs), giving every team member ownership in Grafana Labs' success. We believe in shared outcomes—RSUs help us stay aligned and invested as we scale globally. *Compensation ranges are country specific. If you are applying for this role from a different location than listed above, your recruiter will discuss your specific market’s defined pay range & benefits at the beginning of the process. Why You’ll Thrive at Grafana Labs: 100% Remote, Global Culture - As a remote-only company, we bring together talent from around the world, united by a culture of collaboration and shared purpose. Scaling Organization – Tackle meaningful work in a high-growth, ever-evolving environment. Transparent Communication – Expect open decision-making and regular company-wide updates. Innovation-Driven – Autonomy and support to ship great work and try new things. Open Source Roots – Built on community-driven values that shape how we work. Empowered Teams – High trust, low ego culture that values outcomes over optics. Career Growth Pathways – Defined opportunities to grow and develop your career. Approachable Leadership – Transparent execs who are involved, visible, and human. Passionate People – Join a team of smart, supportive folks who care deeply about what they do. In-Person onboarding - We want you to thrive from day 1 with your fellow new ‘Grafanistas’ to learn all about what we do and how we do it. Balance is Key - We operate a global annual leave policy of 30 days per annum. 3 days of your annual leave entitlement are reserved for Grafana Shutdown Days to allow the team to really disconnect. *We will comply with local legislation where applicable. Equal Opportunity Employer: Grafana Labs is an equal opportunities employer. We welcome applications from everyone regardless of race, colour, nationality, origin, caste, sex, gender reassignment identity or expression, sexual orientation, age, religion or belief, disability, veteran status, genetic information, pregnancy, maternity, marital, family or carer status, or any other characteristic which is protected by local law. We believe that equality and diversity build a strong organisation, and we work hard to ensure that is the foundation of our organisation as we grow. Grafana Labs may utilize AI tools in its recruitment process to assist in matching information provided in CVs to job postings. The recruitment team will continue to review inbound CVs manually to identify alignment with current openings. #LI-Remote For information about how your personal data is used once you’ve applied to a job, check out our privacy policy .

Location: Canada (Remote) Grafana Labs is the company behind Grafana Cloud, the fully managed observability platform trusted by more than 10,000 organizations to ensure reliability, resolve incidents faster, and optimize telemetry at scale. Built on open source and open standards and designed for interoperability across any stack, Grafana Cloud brings AI to observability and observability to AI, giving teams (and their agents) unified visibility so they can see, understand, and act on all their disparate data, wherever it lives, and move at the speed of their ambitions. Customers, including Anthropic, Bloomberg, NVIDIA, Microsoft, and Salesforce, rely on Grafana Labs. We are a 100% remote company with team members across 40+ countries, backed by leading investors including Lightspeed Venture Partners, Sequoia Capital, GIC, Coatue, J.P. Morgan, CapitalG, and Lead Edge Capital. Learn more at grafana.com and follow us on LinkedIn and X . We’re scaling fast and staying true to what makes us different: an open-source legacy, a global collaborative culture, and a passion for meaningful work. Our team thrives in an innovation-driven environment where transparency, autonomy, and trust fuel everything we do. You may not meet every requirement, and that’s okay. If this role excites you, we’d love you to raise your hand for what could be a truly career-defining opportunity. This is a remote opportunity and we are looking for candidates from Canada. Residents of Quebec are not eligible for this role. The Opportunity Grafana Labs is seeking a Staff Engineer (AI & Automation) to own the AI agent infrastructure and automation platform that powers our Marketing Operations organization. You’ll build multi-agent architectures, LLM integrations, and backend services that connect AI models to internal and third-party data platforms. You’ll ship production systems that teams depend on daily. This is a high-autonomy role where you own the technical direction. You’ll identify the highest-leverage problems across Marketing, RevOps, and SDR teams, design the solutions, and ship them. You’ll define the technical direction for the automation platform (data models, API contracts, shared libraries, reference architectures) and partner with Data Engineering, GTM Systems, and Field Operations to build scalable, self-service automation that eliminates manual work and drives operational efficiency. What You'll Be Doing Agentic Systems & AI Infrastructure Own end-to-end development of multi-agent AI systems, from architecture and implementation through testing, deployment, and ongoing operation Build modular, composable agentic systems using orchestration frameworks (LangChain, CrewAI, Anthropic MCP, or similar) that operate 24/7 across teams Develop reusable agentic skills that agents invoke across interfaces (Slack, dashboards, internal apps, CLIs) Implement observability and feedback loops including logging, performance metrics, prompt iteration, model evaluation, and cost management Establish governance and compliance standards for AI workflows including access controls, audit trails, PII handling, and human-in-the-loop escalation paths Systems Integration & Backend Services Build MCP servers, APIs, CLIs, and microservices connecting AI models to business systems (BigQuery, Slack, CRMs, email, calendars, analytics tools) Architect data flows for retrieval-augmented generation (RAG), connecting LLMs to internal knowledge bases, customer data, and real-time business context Build serverless or containerized services (GCP Cloud Functions, Cloud Run) that scale with usage and integrate with Grafana's cloud infrastructure Automation & Workflow Enablement Partner with RevOps, Demand Generation, Regional Marketing, and SDR teams to scope high-impact automation problems, identify bottlenecks, and build solutions with measurable business outcomes Design and deploy workflows using orchestration tools (n8n, Workato, or custom platforms) with CI/CD, testing, and production reliability standards Build systems designed for self-service with documentation, playbooks, and enablement materials that let partner teams operate independently We invest heavily in developer productivity. You'll have access to AI coding assistants (Claude Code, Gemini CLI, OpenAI Codex, and others of your choice within security guidelines). We encourage pragmatic AI-assisted development paired with strong code review and quality standards. What Makes You a Great Fit 8+ years of software engineering experience with depth in backend development, systems integration, or data/analytics engineering 2+ years hands-on experience applying LLMs/AI to production workflows, not just prototypes Strong proficiency in Python and JavaScript/Node.js with Git-based workflows, code review practices, and testing discipline Hands-on experience with LLM frameworks and patterns including prompt engineering, RAG, function calling/tool use, structured output parsing, and evaluation Experience building and operating multi-agent systems at scale including agent decomposition, orchestration patterns (sequential chains, router/dispatcher, parallel fan-out), state management, and production monitoring You diagnose business problems before writing code. You think in workflows and outcomes, not just functions. Deep familiarity with Google Cloud Platform, BigQuery, and serverless/containerized services (Cloud Functions, Cloud Run) Understanding of LLM failure modes and production mitigations including confidence thresholds, fallback logic, human escalation, and cost/latency management Proven ability to identify high-leverage problems, push back on low-impact requests, and deliver end-to-end with minimal direction Fluent with AI-assisted development tools (GitHub Copilot, Cursor, Claude Code). You use AI to build AI systems Clear technical communicator who can explain complex systems in simple terms to both engineers and business stakeholders Bonus Points Experience with vector databases or retrieval pipelines (Pinecone, Weaviate, ChromaDB, Qdrant, pgvector) Familiarity with marketing or sales platforms (Salesforce, Customer.io, HubSpot, Marketo, Outreach) Experience with frontend frameworks (React, Slack Block Kit) for building user-facing AI tool interfaces Observability tooling for AI systems (LangSmith, Weights & Biases, custom evaluation frameworks) Experience with workflow orchestration platforms (n8n, Temporal, Prefect, Airflow) Familiarity with Model Context Protocol (MCP) or similar standards for connecting AI systems to data sources Prior work automating marketing, sales, or customer success workflows in a B2B SaaS environment Active in open-source communities. Grafana is built on OSS and we value engineers who share that DNA In Canada, the base compensation range for this role is CAD 164,490 - CAD 197,389 . Actual compensation may vary based on level, experience, and skillset as assessed throughout the interview process. All of our roles include Restricted Stock Units (RSUs), giving every team member ownership in Grafana Labs' success. We believe in shared outcomes—RSUs help us stay aligned and invested as we scale globally. *Compensation ranges are country specific. If you are applying for this role from a different location than listed above, your recruiter will discuss your specific market’s defined pay range & benefits at the beginning of the process. Why You’ll Thrive at Grafana Labs: 100% Remote, Global Culture - As a remote-only company, we bring together talent from around the world, united by a culture of collaboration and shared purpose. Scaling Organization – Tackle meaningful work in a high-growth, ever-evolving environment. Transparent Communication – Expect open decision-making and regular company-wide updates. Innovation-Driven – Autonomy and support to ship great work and try new things. Open Source Roots – Built on community-driven values that shape how we work. Empowered Teams – High trust, low ego culture that values outcomes over optics. Career Growth Pathways – Defined opportunities to grow and develop your career. Approachable Leadership – Transparent execs who are involved, visible, and human. Passionate People – Join a team of smart, supportive folks who care deeply about what they do. In-Person onboarding - We want you to thrive from day 1 with your fellow new ‘Grafanistas’ to learn all about what we do and how we do it. Balance is Key - We operate a global annual leave policy of 30 days per annum. 3 days of your annual leave entitlement are reserved for Grafana Shutdown Days to allow the team to really disconnect. *We will comply with local legislation where applicable. Equal Opportunity Employer: Grafana Labs is an equal opportunities employer. We welcome applications from everyone regardless of race, colour, nationality, origin, caste, sex, gender reassignment identity or expression, sexual orientation, age, religion or belief, disability, veteran status, genetic information, pregnancy, maternity, marital, family or carer status, or any other characteristic which is protected by local law. We believe that equality and diversity build a strong organisation, and we work hard to ensure that is the foundation of our organisation as we grow. Grafana Labs may utilize AI tools in its recruitment process to assist in matching information provided in CVs to job postings. The recruitment team will continue to review inbound CVs manually to identify alignment with current openings. #LI-Remote For information about how your personal data is used once you’ve applied to a job, check out our privacy policy .

Location: Intellias Meet your recruiter Karolina Sowa karolina.sowa@intellias.com https://www.linkedin.com... Vacancy details Software Engineering Python Engineer Strong Middle Bulgaria, Croatia, Egypt, India, Poland, Portugal, Spain, Ukraine Remote APPLY NOW REFER A FRIEND We are looking for a Python Engineer to build and maintain stateful AI agent workflows using LangGraph. The role focuses on workflow orchestration, checkpointing, human-in-the-loop interactions, resilience patterns, and observability across agent execution paths. The ideal candidate has strong Python expertise, hands-on experience with LangGraph or LangChain, and a solid understanding of asynchronous and distributed workflow design. What project we have for you Our customer is a multinational corporation with more than a century of history and offices in over 180 countries. Their most ambitious goal at the time is to introduce a range of Reduced-Risk Products (RRPs). The target audience is more than 1 billion consumers around the globe. IT platform hosts 700+ applications. Intellia’s mission is to help the client with the engineering of a comprehensive software ecosystem for a game-changing IoT product on the margin of innovative consumer experience and cutting-edge technology. Our teams are involved in the engineering of core platform components for best-in-class eCommerce, Digital Marketing and IoT solutions. As an Engineer, you will become a part of Core Architecture Team and be responsible for the architecture, implementation of best practices in our Digital Engineering Enterprise Platform. The Platform is a set of services and internet applications that accelerate the development and delivery of software applications by taking care of common SDLC challenges. The Platform provides access and consumption for engineering teams to a set of services, technologies, practices for their development and for operating their application, ensuring a set of compliance and best practices. What you will do Design and implement AI agent workflows using LangGraph and Python. Build and maintain graph-based orchestration logic using nodes, edges, and state transitions. Implement workflow checkpointing and recovery mechanisms using SQLite, Redis, or PostgreSQL backends. Develop Human-in-the-Loop (HITL) workflows for approval, escalation, and intervention scenarios. Design resilient execution patterns including retries, timeouts, fallback handling, and failure recovery. Develop asynchronous workflow execution using Python asyncio. Implement OpenTelemetry tracing across orchestration layers and workflow steps. Troubleshoot workflow execution issues and optimize performance, reliability, and observability. Collaborate with platform and AI engineering teams to build scalable agent-driven solutions. Create automated tests for workflow reliability, state transitions, and recovery scenarios. Contribute to engineering standards and best practices around agent orchestration and workflow management. What you need for this • Python (LangGraph nodes, edges, graphs) • Checkpointing with LangGraph (SQLite, Redis, Postgres checkpointers) • HITL workflow implementation • Retry, timeout, fallback patterns in LangGraph • OpenTelemetry tracing across orchestration steps Experience: • 4+ years Python development • LangGraph or LangChain agent orchestration • Async Python (asyncio) Will be a plus: • AWS Bedrock integration • Multi-agent workflow testing • LangSmith trace visualization What it’s like to work at Intellias At Intellias, where technology takes center stage, people always come before processes. By creating a comfortable atmosphere in our team, we empower individuals to unlock their true potential and achieve extraordinary results. That’s why we offer a range of benefits that support your well-being and charge your professional growth. We are committed to fostering equity, diversity, and inclusion as an equal opportunity employer. All applicants will be considered for employment without discrimination based on race, color, religion, age, gender, nationality, disability, sexual orientation, gender identity or expression, veteran status, or any other characteristic protected by applicable law. We welcome and celebrate the uniqueness of every individual. Join Intellias for a career where your perspectives and contributions are vital to our shared success.

- Владеть родительским приложением и бэкендом: привязка устройства, аккаунты, родительский контроль, история активности, удаление данных. - Выпускать релизы iOS и Android со staged rollout и откатами.

Location: Москва Мы - команда, создающая **комплексную систему противодействия мошенничеству** и обеспечивающая online/offline безопасность миллионов клиентов банка 24/7/365, предоставляя инструменты **собственной разработки** для: * Построения графа связей (позволяет проанализировать не только объекты и их свойства, но также их связи с другими объектами различных уровней и характеристик и применяется для получения дополнительных знаний об объекте за счет извлечения знаний о его окружении, выявления наиболее значимой информации, центров влияния, групп связанных объектов, цепочек) * Геопространственой аналитики: анализа локаций, построения маршрутов и тепловых карт * Выявления и анализа попыток мошенничества и иных отклонений * Портфеля технологичных сервисов накопления и обработки больших объемов данных со сложной структурой Мы находимся на передовой технологических инноваций, соединяя возможности ИИ, современные технологии и потребности общества. Наше направление AI-трансформации — это стратегическая инициатива по интеграции LLM-агентов и мультиагентных систем в собственную аналитическую платформу: * AI-решения для глубокого анализа данных * Набор AI Tools, дающих агентам доступ к инструментам и сервисам платформы * Собственная no-code/low-code платформа для оркестрации AI-агентов и автоматизации рабочих процессов * Интеграция с внутренними LLM-шлюзами и средами исполнения агентов * RAG-системы для работы с корпоративными базами знаний и методологиями Наш передовой технологический стек: * Storage - Hadoop, Cassandra, ClickHouse, Elasticsearch, FastGraph, PostgreSQL * Big Data Engineering- Scala, Spark, Flink * Backend - GO (GIN), Scala (ZIO), Kubernetes (OpenShift, DropApp) * Fronted - React, Redux (Redux ToolKit, RTK Query), Typescript, jest. Библиотеки: react-force-graph, leaflet * AI - Python (LangChain, LangGraph, Harness), GO (mcp-go), Typescript (no code platform), ai-coding (gigacode cli, claude, codex), RAG\\GraphRag (PostgreSQL, Elasticsearch, Qdrant) * QA - Java, TestNG, Selenium, Maven, Allure * DevOps - Docker, Helm, Jenkins Обязанности * Проектирование и анализ архитектуры технического решения совместно с командой разработки * Разработка и согласование концепции, проектной документации и технической документации, описание API, ведение документации в Confluence * Развитие направления мультиагентных решений обеспечивая полный цикл реализации * Проведение исследовательских работ и тестирование новых подходов и техник AI * Анализ источников данных, разработка моделей данных, логики алгоритмов и интеграций * Документирование и обеспечение соблюдения правил сбора, хранения и использования данных * Участие в полном цикле разработки и внедрения Требования * Умение быстро погружаться в исследуемую область и оперативно переключаться на новое направление * Навыки интеграции решений на основе ML/AI в реальные производственные системы * Опыт проектирования логической и физической моделей данных. Языки SQL: понимание работы ETL-инструментов, умение писать SQL-запросы различной сложности * Знание форматов JSON/XML и навыки проектирование REST API * Способность обрабатывать и структурировать большой объем сложной информации, доступно и наглядно ее презентовать * Сбор, анализ и документирование бизнес-требований, описание use case, генерация идей, проработка технических решений, интеграционных взаимодействий * Коммуникативные навыки, достаточные для выстраивания конструктивного взаимодействия со смежными подразделениями и внутри команды Будет плюсом * Понимание ключевых алгоритмов и подходов в AI/GenAI (LLM, vector search, NLP, context engineering и др.) * Понимание архитектуры AI/GenAI решений, LLM, RAG, BI и DataOps * Наличие опыта разработки собственных библиотек и инструментов для AI * Опыт успешного внедрения AI-решений, знание инструментов AI на рынке * Опыт программирования на любом языке и способность анализировать код * Владение инструментами Big Data (Hadoop, AirFlow, Spark, Kafka) * Понимание принципов работы NoSQL баз данных (Cassandra, ClickHouse, ElasticSearch, Qdrant, Neo4j) Условия * Комфортный современный офис (ст.м.Кутузовская) * ежегодный пересмотр зарплаты, годовая премия * корпоративный спортзал и зоны отдыха * более 400 образовательных программ СберУниверситета для профессионального и карьерного развития * программа адаптации и помощь руководителя на старте * расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа * гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ * бесплатная подписка СберПрайм+, скидки на продукты компаний-партнеров * вознаграждение за рекомендацию друзей в команду Сбера.

-Проектировать целевую архитектуру системы (модули и компоненты).