AI-инженер (LLM, агенты, AI product) вакансии - удалённо

Location: Morocco Workplace: remote About the Role We are looking for detail-oriented Generative AI Analysts to support a variety of AI training, data annotation, evaluation, and quality review projects.   Project Details Job Title: Generative AI Analyst - Arabic (Morocco) Location: Remote (Morocco) Employment Type: Freelance Pay Rate: 4.5 USD / hour Depending on the project, you may work with text, translations, audio, transcriptions, images, video, or other AI-generated content. Tasks and project requirements may vary based on the program you are assigned to.   Disclaimer  Projects are subject to client requirements and may change. Completion of the application, assessment, and onboarding process does not guarantee assignment to a specific project or a minimum amount of work. This is a human-in-the-loop AI training environment. The use of generative AI tools is prohibited unless explicitly authorized for a specific project. All work must be completed independently using your own knowledge and judgment.  You must comply with all data privacy, security, and confidentiality requirements. Do not store, copy, or share project data outside approved tools and channels. What You’ll Do Review, evaluate, and validate AI-generated content and data according to project guidelines. Compare AI-generated outputs and identify the most accurate, relevant, or high-quality result. Identify errors, inconsistencies, omissions, and quality issues. Annotate, categorize, or label text, audio, images, video, or other data where required. Provide clear written feedback or justifications for your evaluations. Complete assigned tasks independently and within established timelines. Follow project and client guidelines consistently and maintain a high level of accuracy. Attend live office hours and training sessions to familiarize yourself with project guidelines, clarify questions, and prepare for task assignments. Requirements: Native-level fluency in Arabic (Morocco) (written and spoken) . Experience in Generative AI evaluation, data annotation, translation, linguistics, content quality review, transcription, data labeling, or a related field is preferred. Strong English proficiency and written communication skills. Excellent attention to detail, critical thinking, and judgment. Ability to follow detailed guidelines and apply evaluation criteria consistently. Comfortable working independently with structured, repetitive tasks. Familiarity with annotation or evaluation platforms is a plus. Why Join Welo Data?   ✨ Limitless Flexibility   Project-based opportunities that fit your availability. Choose when and how much you want to contribute—fully remote, with complete autonomy.    🌱 Limitless Growth   Optional access to AI and Large Language Model workshops designed specifically for professionals like you. No coding required—just your expertise.    🌍 Limitless Support   Be part of a global contributor community with responsive guidance and support.    💡 Real Impact   Apply your expertise in the Legal field to influence the AI systems shaping the future of your industry—while collaborating with data professionals and expanding your skills.     How to Apply?  Apply now by answering a few quick questions to join our database and become part of our growing community.    About Welo Data  Welo Data, part of Welocalize, is a global AI data company with 500,000+ contributors delivering high-quality, ethical data to train the world’s most advanced AI systems. We’re building smarter, more human AI with a diverse community in 100+ countries.   At Welo Data, Limitless AI. Limitless You. isn’t just a slogan—it’s our promise. We build smarter AI through the power of human contribution, offering limitless opportunities for our global community to grow, contribute, and work on their terms.

Location: Canada Workplace: remote About the Role We are looking for detail-oriented Generative AI Analysts to support a variety of AI training, data annotation, evaluation, and quality review projects.   Project Details Job Title:Generative AI Analyst - French (Canada) Location: Remote (Canada) Employment Type: Freelance Pay Rate: 20USD/per hour Depending on the project, you may work with text, translations, audio, transcriptions, images, video, or other AI-generated content. Tasks and project requirements may vary based on the program you are assigned to.   Disclaimer  Projects are subject to client requirements and may change. Completion of the application, assessment, and onboarding process does not guarantee assignment to a specific project or a minimum amount of work. This is a human-in-the-loop AI training environment. The use of generative AI tools is prohibited unless explicitly authorized for a specific project. All work must be completed independently using your own knowledge and judgment.  You must comply with all data privacy, security, and confidentiality requirements. Do not store, copy, or share project data outside approved tools and channels. What You’ll Do Review, evaluate, and validate AI-generated content and data according to project guidelines. Compare AI-generated outputs and identify the most accurate, relevant, or high-quality result. Identify errors, inconsistencies, omissions, and quality issues. Annotate, categorize, or label text, audio, images, video, or other data where required. Provide clear written feedback or justifications for your evaluations. Complete assigned tasks independently and within established timelines. Follow project and client guidelines consistently and maintain a high level of accuracy. Attend live office hours and training sessions to familiarize yourself with project guidelines, clarify questions, and prepare for task assignments. Requirements: Native-level fluency in French (Canada) (written and spoken) . Experience in Generative AI evaluation, data annotation, translation, linguistics, content quality review, transcription, data labeling, or a related field is preferred. Strong English proficiency and written communication skills. Excellent attention to detail, critical thinking, and judgment. Ability to follow detailed guidelines and apply evaluation criteria consistently. Comfortable working independently with structured, repetitive tasks. Familiarity with annotation or evaluation platforms is a plus. Why Join Welo Data?   ✨ Limitless Flexibility   Project-based opportunities that fit your availability. Choose when and how much you want to contribute—fully remote, with complete autonomy.    🌱 Limitless Growth   Optional access to AI and Large Language Model workshops designed specifically for professionals like you. No coding required—just your expertise.    🌍 Limitless Support   Be part of a global contributor community with responsive guidance and support.    💡 Real Impact   Apply your expertise in the Legal field to influence the AI systems shaping the future of your industry—while collaborating with data professionals and expanding your skills.     How to Apply?  Apply now by answering a few quick questions to join our database and become part of our growing community.    About Welo Data  Welo Data, part of Welocalize, is a global AI data company with 500,000+ contributors delivering high-quality, ethical data to train the world’s most advanced AI systems. We’re building smarter, more human AI with a diverse community in 100+ countries.   At Welo Data, Limitless AI. Limitless You. isn’t just a slogan—it’s our promise. We build smarter AI through the power of human contribution, offering limitless opportunities for our global community to grow, contribute, and work on their terms.

«AI in Product Management» курсы Нөлден бастап 6 айда Product Manager мамандығын меңгересіз. Әр модульдің негізінде — ЖИ құралдары: зерттеу, аналитика, гипотеза тексеру, өнім стратегиясы. Курсты аяқтағанда портфолиоңызда нақты жоба болады. 26 апта / 156 академиялық сағат Толығымен қазақ тілінде Онлайн (Zoom) — Қазақстанның кез келген нүктесінен Алдыңғы IT тәжірибесі қажет емес Маңызды шарт: Оқуға кірісу үшін 100 000 ₸ кепілдік жарна қажет. Курсты толық аяқтағаныңызда — толығымен қайтарылады. Бұл сіздің оқуды аяқтауға деген жауапкершілігіңіздің кепілі. Қалай өтуге болады: 1. WhatsApp-қа «ГРАНТ» деп жазыңыз 2. Маманымыз astanahub.com-да ЭЦП арқылы тіркелуге көмектеседі 3. Анкета толтырып, Docker Academy-ге өтінім бересіз Өтінім қабылдау мерзімі — 7 қыркүйек. Орын саны шектеулі.

Location: Chicago, IL Workplace: Remote ABOUT US At LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale. With $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world. Today, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement. We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater. ABOUT THE TEAM The Deployed Engineering team works directly with companies building and running AI agents in production, helping turn ideas and prototypes into systems teams can rely on. This is a hands-on, highly technical team that partners closely with customer engineers across the full lifecycle, from pre-sales evaluations to post-deployment advisory work. The focus is on achieving the technical win, co-designing agent architectures, and helping customers operate agents reliably at scale using the LangChain suite. Deployed Engineers sit at the intersection of engineering, product, and go-to-market, shaping how LangChain is adopted in the field and feeding real-world insights back into the platform. ABOUT THE ROLE The Deployed Engineer…You’ll work on some of the hardest problems in applied AI — not demos, not research, but systems that real teams depend on in production. The feedback loop is fast, the impact is visible, and the work you do directly shapes how AI agents are built in the real world. This will be a hybrid in office role in Chicago, IL. WHAT YOU’LL DO - Co-architect and co-build production AI agents with customer engineering teams - Own the technical win in pre-sales by designing POCs, answering deep technical questions, and guiding evaluations - Help customers deploy and operate agent-based applications such as conversational agents, research agents, and multi-step workflows - Advise customers post-sale on architecture, best practices, and roadmap-level decisions - Run technical demos, trainings, and workshops for developer audiences - Surface field feedback and contribute reusable patterns, cookbooks, and example code that scale across customers - Occasionally contribute code upstream when it meaningfully improves customer outcomes - Travel to customers up to 25% of the time WHAT YOU’LL BRING - 6+ years in a relevant technical role (software engineering, customer engineering, solutions engineering, founding/product engineering), ideally in a startup or scale-up - Strong Python, JavaScript and systems fundamentals - Have designed agent-based or LLM-powered applications beyond simple API calls, including multi-step workflows, orchestration, and failure handling - Are comfortable working directly with customers during POCs, architecture reviews, and technical evaluations - Can explain technical tradeoffs clearly and build trust with developer audiences - Take responsibility for outcomes, not just recommendations - Have a bias toward action and enjoy figuring things out as you go - Are excited about operating AI agents in production, not just building demos NICE TO HAVE’S - You’ve deployed AI agents in production, especially using LangChain, LangGraph, or similar frameworks - Worked with LLM evaluation, observability, or guardrails - Have experience with cloud environments (AWS, GCP, Azure), containers, and basic Kubernetes concepts - Have shipped and operated production software and are comfortable owning systems under real-world constraints COMPENSATION: Annual OTE range: $200,000–$300,000 USD. Final compensation will depend on experience, skills, and location. Looking for a hands-on implementation role focused on custom delivery, bespoke integrations, and scoped consulting projects? Please check out the Deployed Engineer (Professional Services) roles. 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: — Разрабатывать backend-сервисы, используя Claude Code, Codex или другие AI-агенты для ускорения работы — Проектировать и реализовывать интеграции с внешними API (рекламные сети, трекеры, аналитика, платёжные системы) — Автоматизировать бизнес-процессы — сбор данных, отчётность, мониторинг — Прототипировать решения: от идеи до рабочего MVP за часы — Ревьюить и рефакторить код — как свой, так и сгенерированный агентом — Настраивать окружение для AI-агентов: инструкции, правила, контекст проекта Требования: — Python: коммерческий опыт от 2 лет. Уверенное владение asyncio, FastAPI или Flask, SQLAlchemy или аналоги. Понимание GIL, менеджеров контекста, декораторов, генераторов — не на уровне “знаю что это”, а используете в работе — Go (будет серьёзным плюсом): goroutines, channels, стандартная библиотека, понимание concurrency-модели — Базы данных: PostgreSQL или MySQL на уровне написания сложных запросов, понимание индексов, миграций, оптимизации — Опыт с Redis — API: проектирование и реализация REST API, работа с вебхуками, OAuth, rate limiting. Опыт интеграции с внешними сервисами в продакшене — Git: ветвление, ребейз, черри-пик, решение конфликтов — не гуглите каждый раз — Инфраструктура: Docker на уровне написания и отладки Dockerfile и docker-compose. Базовое понимание CI/CD — Linux: уверенная работа в терминале, дебаг на удалённом сервере, чтение логов Требования по AI-инструментам: — Реальный опыт работы с Claude Code, Codex, Cursor, Aider или другими AI-агентами — именно как инструмент в ежедневной разработке — Умение декомпозировать задачу для агента, управлять контекстом, задавать ограничения — Понимание, где агент силён, а где стабильно ошибается — и привычка проверять результат Не подойдёт, если: — Вы пришли в программирование через AI и не писали код самостоятельно до появления ChatGPT — Ваш коммерческий опыт меньше 1.5 лет — Вы не можете без подсказки объяснить, чем отличается процесс от потока, зачем нужен индекс в БД или как работает HTTP-запрос на уровне TCP — Ваш опыт с AI — только браузерный чат, без терминальных или IDE-агентов Условия: — Удалённо, ПН–ПТ, 10:00–19:00 МСК — Выплаты: от 1200-1500$ в месяц (по результатам собеседования, в дальнейшем возможен рост) — Оплачиваемая стажировка до 2 недель (полная ставка) — Выплаты 1 и 16 числа (USDT BEP-20) — Все расходы на AI-инструменты покрывает компания — Подписание договора NDA (по паспортным данным и видеоверификации)

Location: Remote, Global ABOUT SUPABASE Supabase is the Postgres development platform, built by developers for developers. We provide a complete backend solution including Database, Auth, Storage, Edge Functions, Realtime, and Vector Search. All services are deeply integrated and designed for growth. ABOUT THE ROLE We are hiring a AI Platform Engineer to build the execution layer for Supabase's internal AI systems. Supabase is building an AI-native internal operating system: a common way of working across the company where AI carries a meaningful share of the operational load rather than sitting alongside it as an assistant. We are standing up a new central team to build those systems, enable the teams, and embed AI operations throughout the organization. You are the engineer on that team. The execution layer is yours. You will build the platform that actually runs agents: an event-triggered queue, a headless model-agnostic runtime, durable state so work survives a restart, a human review gate, atomic rollback, and full logging of every prompt, tool call and decision so any run can be reconstructed. You will build the evaluation layer that makes any of it trustworthy, because an agent that cannot be measured cannot be trusted with anything beyond reading. And you will build the agents themselves, across everything from executive reporting down to a layer of agents that watch the platform and improve it. This is a governance-heavy environment by design, and that is the interesting part of the problem. Agents are risk-tiered from read-only internal data through to external-facing output, with review depth, evaluation requirements and human approval scaling by tier. Some capabilities are permanently off limits: an agent may read and report, it may carry a human-authored update into a system of record once a human consents, and it may initiate contact within a strict budget, but it may never autonomously write a commitment (an owner, a due date, a status) into a shared work system. Your job is to make that structurally impossible rather than merely forbidden. You will be the only engineer on this platform. You will close open architecture decisions yourself, own the infrastructure end to end, and instrument the system so it reports its own return. WHAT YOU'LL BE RESPONSIBLE FOR In this role, you'll: - Ship the agent platform to production. An event-triggered queue, a headless model-agnostic runtime (choosing the runtime is an open decision you will close), durable state that survives a failed run, a human review gate, atomic rollback, and complete run logging in the warehouse. - Own the evaluation layer, and switch on the gate that depends on it. Golden suites with behavioral assertions rather than intuition, judge criteria with a written rubric, safety cases that must pass on every run, and a CI gate that blocks a regression from merging. This is the precondition for every agent that does anything beyond read internal data. - Build and register the agent portfolio. Reporting, drafting, linting, triage and question-answering agents across the executive, team-lead and individual-contributor layers, plus a meta layer that observes the platform and improves it. - Enforce governance in code. Risk tiers the pipeline actually enforces, least-privilege credentials per agent, tool-permission gates, a decision audit log, and autonomy classes where the dangerous class has no code path rather than a warning label. Nothing runs without a registered owner, tier, tool grant and human gate. - Design how the system contacts people. A hard interruption budget per person, message bundling instead of a stream of pings, and a structure that gives something useful before it asks for anything. Adoption depends on this more than on any other single design choice. - Own the platform tooling. The compiler and validator, inventory integrity, and the paths that distribute context and capabilities into the repositories and chat surfaces where work happens. - Compute the operating measures from production data. A pipeline from raw system activity through to a computed maturity grade per team, defensible enough that a team can dispute the result and be answered with the query rather than an opinion. - Instrument the platform's own return. A ledger that logs the work each agent absorbs and computes the monthly figure, so the value of the system is a measurement rather than a claim. HOW YOU'LL THINK RECURSIVE THINKING You build the generator, not the artifact. When you need thirty agents, you do not write thirty agents; you build the inventory, the compiler and the distribution path that makes the thirty-first cost an afternoon, then a meta layer whose job is to watch the platform, find its drift and file the fix. The evaluation layer is the same move applied to trust. You are not checking whether one agent is correct today. You are building the machine that decides whether every future agent is allowed to ship, which means the suite has to be right in a way the agent does not, and the thing that grades has to be graded too. INVERSION THINKING You start from the failure and work backward to the design. The rule is that an agent may never autonomously write a commitment into a shared work system. The weak implementation is an instruction in a prompt. The strong one is that the credential in the agent's tool grant physically cannot set an owner, a due date or a status, so no amount of clever input, prompt injection or model error produces the forbidden write. You reach for the second one first. Same for evaluation. Before writing a suite you enumerate how the agent can be wrong: an update that invents progress that did not happen, one that quotes a private channel into a public digest, one that is accurate and reads as an accusation, one that credits the wrong person. Then the suite is that list, each case caught before the agent ships rather than after it embarrasses someone. AI-NATIVE EXECUTION You use agentic tools on real work, in files and repositories, with the same rigor you apply to anything else you ship. You have a setup of your own and can describe it mechanically: what triggers it, what it is allowed to touch, where the human approves, what it logs, and how you found out the one time it went wrong. That experience is this job, generalized. You are building for async engineers across 40+ countries who will judge the platform by whether it saves them an hour or costs them one, so you are your own first user and your own harshest reviewer. YOU MIGHT BE A GOOD FIT IF YOU MUST HAVE - Have shipped production LLM agent systems that other people depended on. Not demos, not internal showcases. Systems with operational history, real users and at least one incident you can talk through. Prompt engineering on its own does not clear this bar. - Design evaluations, not spot checks. You build golden sets, write behavioral assertions, define judge rubrics, set pass thresholds and gate CI on the result. You can explain why "we reviewed a bunch of outputs and they looked good" is not evaluation. - Have done deep API work against the systems work actually lives in, and have authored MCP servers. You know the specific failure modes of those APIs, not just that they exist. - Own infrastructure end to end in Python on GCP, with a cloud warehouse and infrastructure as code. You provision, deploy, monitor and roll back your own systems, and you close architecture decisions rather than routing them onward. - Have taste about how software contacts humans. You treat every notification as spending a limited amount of trust. STRONG SIGNAL - Public work in this space. An open-source agent framework, an MCP server, an evaluation harness, or writing on agent reliability that other practitioners cite. - LLM observability and cost instrumentation. Tracing agent runs, attributing spend per run, and building the queries that turn raw logs into a report someone acts on. - You have built an internal platform that non-engineers adopted voluntarily, and can describe what you changed after watching them use it. WHAT SUCCESS LOOKS LIKE - The platform is boring. Agents run on a schedule and on events, state survives restarts, failed runs roll back cleanly, and every run can be reconstructed from its log. - Nothing ships unevaluated. Every registered agent has a real suite with safety cases, the gate blocks regressions, and when someone challenges an output the answer is a test case rather than an argument. - The dangerous action is impossible, not discouraged. An audit of any agent's credentials shows it cannot perform the writes it is not allowed to perform, and the audit log makes every consequential decision traceable. - Teams pull the platform instead of being pushed. Agents get adopted because the reports are useful and the contact is rare and well-timed, and each team ends up with at least one workflow that runs automatically. - The platform reports its own value. The work absorbed is measured and published, so the case for expanding it is made with data rather than enthusiasm. 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.

Задачи: - Разработка и развитие backend-компонентов и микросервисов на Python (FastAPI, asyncio). - Разработка и развитие интеграционных решений для взаимодействия с внутренними и внешними сервисами посредством REST API. - Разработка и развитие компонентов, связанных с применением технологий Generative AI и AI-агентов. - Разработка и развитие высоконагруженных сервисов с использованием PostgreSQL, Redis и современных подходов к работе с данными. - Разработка и развитие асинхронных процессов обработки данных и механизмов обмена сообщениями. - Разработка и развитие решений по логированию, мониторингу и наблюдаемости сервисов. - Участие в проектировании архитектуры решений, проработке контрактов взаимодействия между сервисами и выборе технических подходов. - Участие в тестировании разрабатываемого функционала и обеспечении качества кода. - Взаимодействие с командой разработки, аналитиками, архитекторами и QA-инженерами при реализации задач проекта. Требования: - Опыт коммерческой разработки на Python от 3-4 лет. - Уверенное знание Python 3.9+. - Опыт разработки микросервисной архитектуры и REST API. - Практический опыт работы с FastAPI, asyncio, aiohttp или httpx. - Опыт работы с PostgreSQL, Redis, SQLAlchemy. - Понимание принципов асинхронного программирования. - Опыт контейнеризации приложений с использованием Docker и понимание основ Kubernetes. - Опыт проектирования и реализации интеграционных решений. - Опыт написания тестов (pytest, unittest, pytest-mock). - Знание подходов к логированию и мониторингу приложений. - Понимание принципов построения высоконагруженных систем.

Location: Remote - United States What We’re Building Honeycomb is a service for the near and present future, defining observability and raising expectations of what developer tools can do! We’re working with well known companies like HelloFresh, Slack, LaunchDarkly, and Vanguard and more across a range of industries. This is an exciting time in our trajectory, we’ve closed Series D funding, scaled past the 200-person mark, and were named to Forbes’ America’s Best Startups of 2022 and 2023! If you want to see what we’ve been up to, please check out these blog posts and Honeycomb.io press releases . Who We Are We come for the impact, and stay for the culture! We’re a talented, opinionated, passionate, fiercely inclusive, and responsible group of bees. We have conviction and we strive to live our values every day. We want our people to do what they truly love amongst a team of highly talented (but humble) peers. How We Work We are a fully distributed company, which means we believe it is not where you sit, but how you deliver that matters most. We invest in our people and care about how you orient to our culture and processes. At the same time we imbue a lot of trust, autonomy, and accountability from Day 1. #LI-Remote Little more about the team: AI agents at Honeycomb investigate, reason, and act on real observability data. They live in Canvas: the agentic workspace where engineers go to understand their systems. The Agentic Intelligence team has shipped Canvas, the Honeycomb MCP server, and the Canvas Agent and Canvas Skills surfaces. What we're looking for today is someone who brings deep agent expertise and uses it to expand what the team can build: new agents, new surface area in Canvas, memory, spatial awareness, improved performance on our Bedrock loop. Honeycomb's data store is fast and accepts high cardinality data; that's what makes agents built on top of it different from anything built on a conventional observability backend. This role is about taking advantage of that building agents that can do things no other observability product can do because the underlying data makes it possible. Some of this work will start as a prototype. The expectation is that the code makes it through the full arc, from the rough first version through to something that holds up in production. What you'll do Design and deliver production-grade agents. Build agents that investigate, reason, and act on live observability data inside Canvas. These agents must be trustworthy to engineers in high pressure situations, including mid-incident. Take one from rough first version to something that holds up under production traffic. Own the agent work; support the whole product. Scope, build, ship, and maintain the agents including the evals that tell you whether they got better or are just different. This role is agent-focused and also includes some fullstack development. Build agents only Honeycomb can build. Use a data store that returns high-cardinality queries in seconds to reason over signal a conventional backend can't serve at this fidelity correlating across services, drilling into a single trace, comparing before and after a deploy. Extend the surface, and decide what's next. Ship new capability into Canvas, the MCP server, and Canvas Skills memory, spatial awareness, a faster Bedrock loop and make the case for what comes after with working code. Distinguish hype from signal in a field with plenty of both. Define what "good" means for agents here. Set the bar: measurable against real evals, maintainable, and honest about their limits. Example projects Multiple agents collaborating on one shared Canvas investigation each claiming a hypothesis, publishing findings, and narrowing the search space for the others so it resolves faster (blog). Auto-investigation the moment an SLO burn alert fires the agent forms hypotheses and prepares visualizations before a human looks, cutting mean-time-to-insight for on-call (o11ycon 2026). Skills that encode a team's domain expertise e.g. Kubernetes thresholds so agents and human colleagues can lean on them (o11ycon 2026). What you'll bring: AI and agent engineering experience. You've shipped LLM-based systems people relied on in production not demos, not fine-tuned models in a research context. You know where agent systems break and how to design around it. End-to-end ownership. On a small team there's no handoff queue. You can take something from rough prototype to production-grade without needing someone behind you to do the durable engineering. Current judgment, not just past experience. You have informed opinions about what's shifted in agent design in the last six to twelve months that would change how you'd build today. Agent architecture depth. You understand how a fast, high-cardinality data store changes what an agent can reason about, and how to design for that. Product judgment. You can look at what the agent layer does today and see what it should do next and make that case with a prototype, not a deck. Even better Observability or developer-tools background. Engineers are your users; you'll ramp faster with fluency in that world, and the work is better. Familiarity with eval frameworks, agent tooling, RAG, and prompt engineering. Base Salary based on level of experience $183,340 — $206,000 USD What you'll get when you join the Hive: A stake in our success - generous equity with employee-friendly stock program It’s not about how strong of a negotiator you are - our pay is based on transparent levels relative to experience Time to recharge with unlimited PTO A distributed-first mindset and culture (really!) Home office, co-working, and internet stipend Full benefits coverage for employees, with additional coverage available for dependents Up to 16 weeks of paid parental leave, regardless of path to parenthood Annual development allowance And much more... Please note we cannot currently sponsor or support visa transfers at this time. Additionally, in compliance with applicable law, all persons hired will be required to verify identity and eligibility to work. Phishing and Recruitment Scam Warning: We take your security seriously. Please be aware that recruitment scams are increasingly common and scammers may create email addresses or websites to impersonate Honeycomb employees. To help protect you: All communications will come from an @honeycomb.io email address We occasionally work with external recruiting agencies. These partners will use legitimate business email addresses— never personal accounts like Gmail or Yahoo . Our recruiting process will never ask you to provide financial or sensitive personal information, including but not limited to: Social security or tax identification numbers Credit card numbers Bank account information Diversity & Accommodations: We're committed to building a diverse, inclusive, and equitable workplace—where people of all backgrounds, identities, experiences, and abilities are welcomed, valued, and supported. We recognize that there is no single path to success and embrace nontraditional career journeys and diverse perspectives as key to building stronger, more innovative teams. We strive to ensure an inclusive experience throughout every stage of our hiring process and are happy to provide reasonable accommodations as needed. If you require accommodations or accessible formats at any point during our hiring process, please let your recruiter know. As an equal opportunity employer our hiring process is designed to put you at ease and help you show your best work. If there’s anything we can do to improve your experience, we’re always open to feedback. Privacy Notice: If you apply for a job at Honeycomb and your application is unsuccessful (or you withdraw from the process or decline our offer), Honeycomb will retain your information after your application for a period of time in accordance with local laws. We retain this information for various reasons, including in case we face a legal challenge in respect of a recruitment decision, to consider you for other current or future jobs at Honeycomb, and to help us better understand, analyze and improve our recruitment processes. For more information regarding our privacy practices please see the Honeycomb Privacy Notice . If you do not want us to retain your information, or want us to update it, please contact privacy@honeycomb.io . Please note, however, that we may retain some information if required by law or as necessary to protect ourselves from legal claims.

Задачи: — Участие в двух треках: Инкубатор (формирование команд с нуля вокруг реальных отраслевых проблем) и Акселератор (вывод существующих AI продуктов и исследований до MVP, пилота и реальных клиентов). — В августе провести валидацию проблемы с 10 интервью с клиентами. — В сентябре создать работающий MVP и привлечь первого внешнего пользователя. — В октябре запустить пилот с клиентом. — Достичь критерия выпуска: реальное развертывание продукта. Требования: — Быть студентом, исследователем, преподавателем, выпускником или амбициозным кандидатом, желающим создавать AI продукты для глобальных рынков. — Владение английским языком. Условия: — 12 недель интенсивной работы. — Работа с основателями и менторами из стартапов, венчурных фондов и глобальных технологических компаний из разных стран. — Целевые результаты: 15+ MVP, 3+ пилотных проектов с клиентами. — Отбор на основе интервью. Организаторы: — AI Institute — Institute of Automation and Information Technologies, Satbayev University Партнеры: — Center of Mathematical Finance (CMF) — Association for the Development of Artificial Intelligence in Education (Kazakhstan)

Location: New York; Massachusetts; Washington DC-Baltimore Area; Pennsylvania Workplace: remote About the role: We’re an Anthropic and AWS Partner with 150+ certified architects. That means the newest models, real budgets, and problems that matter — without the layers, politics, and years-to-impact of a hundred-thousand-person firm. 350+ builders, 100+ customers in production, founder-led since 2010, offices across North America, LATAM, and EMEA. Why Provectus Frontier access — Anthropic Preferred Partner: newest Claude models, real budgets, real problems Real tasks (not scope), real deployment. You go where the operating problem is and ship production code to real users, not slide decks that sit in a drive Embedded, not engaged. You are part of the customer’s team and inside their process — not a vendor running a project alongside it. Embedded is not staff-augmented Autonomous. You own the work from framing through to production, visible to clients and founders.  You own the method; nobody hands you a ticket Your experience stays portable. Every engagement is a new problem, often in a new industry, so your skills compound instead of narrowing to one product. No legacy-maintenance grind, not a single stack Craft & growth — build beside senior FDEs on a Claude-certified team; a real platform (talks, OSS) to grow your name We give you a stage: Claude certification, conference talks, open-source contributions, published work under your own name What You’ll Do: Spend the first weeks of each engagement in the operator’s seat, learning the constraints from the inside, then rebuild the function from first principles alongside the client and a Forward Deployed Executive — and you are the one who builds it Be part of the customer’s team, inside their process; work on real tasks, not scoped deliverables, and own the method autonomously You won’t start from zero. Provectus builds industry Blueprints — working systems that have already shipped for a customer in your industry. Your engagement starts from that baseline, and what you learn in the field goes back into it Deliver across Cowork Activation, Agentic SDLC, and Blueprint Activations in Financial Services and Healthcare Help shape how leading enterprises adopt AI, from strategy through first deployment; help build the tooling and frameworks of a growing AI delivery practice What We Look For: Typically 7+ years building software, a substantial share of it writing production code you were accountable for. You are hands-on today and intend to stay that way Genuinely willing to spend time understanding someone else’s job before writing code; demonstrated ability to become conversant in an unfamiliar business function quickly Shipped GenAI/LLM systems to production — not demos, not notebooks. You’ve handled the parts that get hard after the prototype works You evaluate. You have built or owned an eval suite for a non-deterministic system, and you can explain what you measured and why Strong engineering fundamentals — dropped into an unfamiliar codebase or language, you’re productive. Python and/or TypeScript proficiency; depth matters more than stack Cloud-native delivery on AWS (GCP/Azure a plus), including the operational reality of a system someone else inherits Pre-sales experience. Credible with senior stakeholders — you can hold a redesign conversation with a BU head and a scoping conversation with a CTO without losing either room Comfort with ambiguity and ownership. Engagements start underspecified by design. Closing that gap is the job Hands-on experience with Claude, Claude Code, and Cowork (skills, workflows, MCP, integrations, connectors, custom harnesses) Nice to have: Containers and orchestration (Kubernetes/ECS) and CI/CD in production IaC depth: AWS CDK, CloudFormation, Terraform Open-source contributions or public writing on applied AI What We Offer: Full-time employment On-site visits to clients’ facilities and our office in NYC 1/week Unlimited Vacation policy Generous health, vision, and dental insurance 401(K) matching plan Base salary $180k–$200k + bonuses; OTE up to $250k. The salary range is determined through interviews and a review of the education, experience, knowledge, skills, and abilities of the applicant, and alignment with market data No Visa sponsorship How we hire: Recruitment interview. Tech interview with our FDE (30 min). A live engineering building session (1.5 hours). We hand you a business function and the constraints. You have to understand the job and rebuild it — then say what you’d build and how you’d know it worked. Use AI the way you normally would; we expect AI-generated code. What we look at is how you used AI, the judgment behind your choices, and what you researched beyond what any LLM already knows. 4. Soft skills interview. 5. Offer discussion. We’re real: A person reads your application. We filter out AI-generated and fraudulent submissions automatically. Every application that clears that filter is read by someone on our team We may ask for a quick ID verification. Fraudulent applications are common, and this protects real candidates You’ll meet the people you’d work with. We host AI engineering events in New York and San Francisco. We'd be happy to talk in person. Your interviews are with Pavel Borobov, Solutions Architect, and Stepan Pushkarev, Co-Founder & CEO

Задачи: — Работать над повышением стабильности и улучшением производительности платформы; — Сопровождать клиентские запуски совместно с клиентским менеджером и заказчиком; — Разрабатывать новые интеграции и дорабатывать функционал; — Создавать и настраивать автоматизации на платформе; — Разбирать тикеты и фиксить баги. Требования: — Хорошо знать современный Python, фреймворки не принципиальны; — Иметь опыт разработки функционала, связанного с LLM, знакомство с OpenAI API, принципами составления промптов, tool calling, prompt caching, RAG; — Уметь использовать ИИ в работе: составлять требования, проверять, тестить, а не просто ваншотить; — Иметь опыт с API и интеграциями в основных российских CRM (AmoCRM, Bitrix24 и тд); — Проактивность, готовность учиться новому и внедрять лучшие практики. Будет плюсом: — Наличие pet-проектов, связанных с LLM/AI; — Хорошее знание SQL и PostgreSQL / Clickhouse; — Техническое образование; — Опыт работы в небольших командах / стартапах; Условия: — Работа в быстрорастущем стартапе с максимально разнообразными задачами и быстрым ростом.

Location: New Zealand; Remote, Worldwide; Remote - Europe; Remote Workplace: remote Welo Data, part of Welocalize, is a global AI data company with 500,000+ contributors delivering high-quality, ethical data to train the world’s most advanced AI systems. We’re building smarter, more human AI with a diverse community in 100+ countries. At Welo Data, Limitless AI. Limitless You. isn’t just a slogan—it’s our promise. We build smarter AI through the power of human contribution, offering limitless opportunities for our global community to grow, contribute, and work on their terms. Are you detail-oriented and curious about tech?   Welo Data is building a global network of linguists, language enthusiasts, and culturally aware contributors to shape safer, smarter AI.   By joining our talent community, you’ll be first in line for flexible, remote projects in annotation, evaluation, and prompt creation—always on your terms.   Note: This is not an active job opening. By applying, you’ll join our global contributor network. When a project aligns with your skills and availability, we’ll reach out with all the details, including scope, timeline, and compensation.   What You’ll Need - Native or near-native fluency in Maori - Based in: Worldwide, Remote - English proficiency - Comfortable with digital tools - Detail-oriented and curious   Why Join Us? 🚀 Limitless You – Work when and where you want, with room to grow 🤖 Limitless AI – Contribute to smarter, more inclusive AI 🌎 Be Part of Us – Join a global, supportive community   About our projects: Welo Data projects vary by scope and complexity, but all play a role in building more accurate, inclusive, and human-centered AI.   Depending on your skills, you might: 📝 Annotate or label text, images, or audio to train AI systems 🔍 Evaluate search results, ads, or chatbot responses for quality and relevance 💬 Create and test prompts to improve large language models (LLMs) 🌍 Ensure content reflects cultural, linguistic, or industry-specific accuracy Some projects last a few days; others may extend over several weeks. Before each one, you’ll receive clear details on tasks, timelines, and compensation, so you can decide what works best for you.   How to Apply? Click the “Apply” button on our website to fill out a short form and join our talent database. It only takes a few minutes to get started and become part of our growing community.   📬Questions? If you have any questions about the process, feel free to contact us at TalentRequests@welocalize.com.

Обязанности: - Запускать и развивать сервисы, которые основаны на ML-моделях и LLM - Работать над проектами от идеи до продакшена: анализировать потребности, проектировать архитектуру, разрабатывать и внедрять проекты - Интегрировать модели в удобные для сотрудников интерфейсы - Вместе с командой влиять на технические решения и стандарты, внедрять лучшие практики Требования: - Опыт работы с Python 3 - Умение работать с FastAPI и базами данных PostgreSQL, контейнерами в Docker и кластерами Kubernetes - Умение писать тесты на Pytest - Интерес к миру ML на уровне доменной области - Самостоятельность и готовность нести ответственность за проект

Location: Indonesia; Remote - Europe; Remote, Worldwide Workplace: remote Welo Data, part of Welocalize, is a global AI data company with 500,000+ contributors delivering high-quality, ethical data to train the world’s most advanced AI systems. We’re building smarter, more human AI with a diverse community in 100+ countries. At Welo Data, Limitless AI. Limitless You. isn’t just a slogan—it’s our promise. We build smarter AI through the power of human contribution, offering limitless opportunities for our global community to grow, contribute, and work on their terms. Are you detail-oriented and curious about tech?   Welo Data is building a global network of linguists, language enthusiasts, and culturally aware contributors to shape safer, smarter AI.   By joining our talent community, you’ll be first in line for flexible, remote projects in annotation, evaluation, and prompt creation—always on your terms.   Note: This is not an active job opening. By applying, you’ll join our global contributor network. When a project aligns with your skills and availability, we’ll reach out with all the details, including scope, timeline, and compensation.   What You’ll Need - Native or near-native fluency in Tajik - Based in: Worldwide, Remote - English proficiency - Comfortable with digital tools - Detail-oriented and curious   Why Join Us? 🚀 Limitless You – Work when and where you want, with room to grow 🤖 Limitless AI – Contribute to smarter, more inclusive AI 🌎 Be Part of Us – Join a global, supportive community   About our projects: Welo Data projects vary by scope and complexity, but all play a role in building more accurate, inclusive, and human-centered AI. Depending on your skills, you might: 📝 Annotate or label text, images, or audio to train AI systems 🔍 Evaluate search results, ads, or chatbot responses for quality and relevance 💬 Create and test prompts to improve large language models (LLMs) 🌍 Ensure content reflects cultural, linguistic, or industry-specific accuracy Some projects last a few days; others may extend over several weeks. Before each one, you’ll receive clear details on tasks, timelines, and compensation, so you can decide what works best for you.   How to Apply? Click the “Apply” button on our website to fill out a short form and join our talent database. It only takes a few minutes to get started and become part of our growing community.   📬 Questions? If you have any questions about the process, feel free to contact us at TalentRequests@welocalize.com.

Location: United States; Remote - Europe; Remote, Worldwide Workplace: remote Welo Data, part of Welocalize, is a global AI data company with 500,000+ contributors delivering high-quality, ethical data to train the world’s most advanced AI systems. We’re building smarter, more human AI with a diverse community in 100+ countries. At Welo Data, Limitless AI. Limitless You. isn’t just a slogan—it’s our promise. We build smarter AI through the power of human contribution, offering limitless opportunities for our global community to grow, contribute, and work on their terms. Are you detail-oriented and curious about tech?   Welo Data is building a global network of linguists, language enthusiasts, and culturally aware contributors to shape safer, smarter AI.   By joining our talent community, you’ll be first in line for flexible, remote projects in annotation, evaluation, and prompt creation—always on your terms.   Note: This is not an active job opening. By applying, you’ll join our global contributor network. When a project aligns with your skills and availability, we’ll reach out with all the details, including scope, timeline, and compensation.   What You’ll Need - Native or near-native fluency in Somali - Based in: Worldwide, Remote - English proficiency - Comfortable with digital tools - Detail-oriented and curious   Why Join Us? 🚀 Limitless You – Work when and where you want, with room to grow 🤖 Limitless AI – Contribute to smarter, more inclusive AI 🌎 Be Part of Us – Join a global, supportive community   About our projects: Welo Data projects vary by scope and complexity, but all play a role in building more accurate, inclusive, and human-centered AI. Depending on your skills, you might: 📝 Annotate or label text, images, or audio to train AI systems 🔍 Evaluate search results, ads, or chatbot responses for quality and relevance 💬 Create and test prompts to improve large language models (LLMs) 🌍 Ensure content reflects cultural, linguistic, or industry-specific accuracy Some projects last a few days; others may extend over several weeks. Before each one, you’ll receive clear details on tasks, timelines, and compensation, so you can decide what works best for you.   How to Apply? Click the “Apply” button on our website to fill out a short form and join our talent database. It only takes a few minutes to get started and become part of our growing community.   📬 Questions? If you have any questions about the process, feel free to contact us at TalentRequests@welocalize.com.

Location: Philippines; Remote - Europe; Remote, Worldwide Workplace: remote Welo Data, part of Welocalize, is a global AI data company with 500,000+ contributors delivering high-quality, ethical data to train the world’s most advanced AI systems. We’re building smarter, more human AI with a diverse community in 100+ countries. At Welo Data, Limitless AI. Limitless You. isn’t just a slogan—it’s our promise. We build smarter AI through the power of human contribution, offering limitless opportunities for our global community to grow, contribute, and work on their terms. Are you detail-oriented and curious about tech?   Welo Data is building a global network of linguists, language enthusiasts, and culturally aware contributors to shape safer, smarter AI.   By joining our talent community, you’ll be first in line for flexible, remote projects in annotation, evaluation, and prompt creation—always on your terms.   Note: This is not an active job opening. By applying, you’ll join our global contributor network. When a project aligns with your skills and availability, we’ll reach out with all the details, including scope, timeline, and compensation.   What You’ll Need - Native or near-native fluency in Tagalog - Based in: Philippines - English proficiency - Comfortable with digital tools - Detail-oriented and curious   Why Join Us? 🚀 Limitless You – Work when and where you want, with room to grow 🤖 Limitless AI – Contribute to smarter, more inclusive AI 🌎 Be Part of Us – Join a global, supportive community   About our projects: Welo Data projects vary by scope and complexity, but all play a role in building more accurate, inclusive, and human-centered AI. Depending on your skills, you might: 📝 Annotate or label text, images, or audio to train AI systems 🔍 Evaluate search results, ads, or chatbot responses for quality and relevance 💬 Create and test prompts to improve large language models (LLMs) 🌍 Ensure content reflects cultural, linguistic, or industry-specific accuracy Some projects last a few days; others may extend over several weeks. Before each one, you’ll receive clear details on tasks, timelines, and compensation, so you can decide what works best for you.   How to Apply? Click the “Apply” button on our website to fill out a short form and join our talent database. It only takes a few minutes to get started and become part of our growing community.   📬 Questions? If you have any questions about the process, feel free to contact us at TalentRequests@welocalize.com.

Location: Philippines; Remote - Europe; Remote, Worldwide Workplace: remote Welo Data, part of Welocalize, is a global AI data company with 500,000+ contributors delivering high-quality, ethical data to train the world’s most advanced AI systems. We’re building smarter, more human AI with a diverse community in 100+ countries. At Welo Data, Limitless AI. Limitless You. isn’t just a slogan—it’s our promise. We build smarter AI through the power of human contribution, offering limitless opportunities for our global community to grow, contribute, and work on their terms. Are you detail-oriented and curious about tech?   Welo Data is building a global network of linguists, language enthusiasts, and culturally aware contributors to shape safer, smarter AI.   By joining our talent community, you’ll be first in line for flexible, remote projects in annotation, evaluation, and prompt creation—always on your terms.   Note: This is not an active job opening. By applying, you’ll join our global contributor network. When a project aligns with your skills and availability, we’ll reach out with all the details, including scope, timeline, and compensation.   What You’ll Need - Native or near-native fluency in Cebuano - Based in: Worldwide, Remote - English proficiency - Comfortable with digital tools - Detail-oriented and curious   Why Join Us? 🚀 Limitless You – Work when and where you want, with room to grow 🤖 Limitless AI – Contribute to smarter, more inclusive AI 🌎 Be Part of Us – Join a global, supportive community   About our projects: Welo Data projects vary by scope and complexity, but all play a role in building more accurate, inclusive, and human-centered AI. Depending on your skills, you might: 📝 Annotate or label text, images, or audio to train AI systems 🔍 Evaluate search results, ads, or chatbot responses for quality and relevance 💬 Create and test prompts to improve large language models (LLMs) 🌍 Ensure content reflects cultural, linguistic, or industry-specific accuracy Some projects last a few days; others may extend over several weeks. Before each one, you’ll receive clear details on tasks, timelines, and compensation, so you can decide what works best for you.   How to Apply? Click the “Apply” button on our website to fill out a short form and join our talent database. It only takes a few minutes to get started and become part of our growing community.   📬 Questions? If you have any questions about the process, feel free to contact us at TalentRequests@welocalize.com.

Location: Indonesia; Remote - Europe; Remote, Worldwide Workplace: remote Welo Data, part of Welocalize, is a global AI data company with 500,000+ contributors delivering high-quality, ethical data to train the world’s most advanced AI systems. We’re building smarter, more human AI with a diverse community in 100+ countries. At Welo Data, Limitless AI. Limitless You. isn’t just a slogan—it’s our promise. We build smarter AI through the power of human contribution, offering limitless opportunities for our global community to grow, contribute, and work on their terms. Are you detail-oriented and curious about tech?   Welo Data is building a global network of linguists, language enthusiasts, and culturally aware contributors to shape safer, smarter AI.   By joining our talent community, you’ll be first in line for flexible, remote projects in annotation, evaluation, and prompt creation—always on your terms.   Note: This is not an active job opening. By applying, you’ll join our global contributor network. When a project aligns with your skills and availability, we’ll reach out with all the details, including scope, timeline, and compensation.   What You’ll Need - Native or near-native fluency in Sundanese - Based in: Worldwide, Remote - English proficiency - Comfortable with digital tools - Detail-oriented and curious   Why Join Us? 🚀 Limitless You – Work when and where you want, with room to grow 🤖 Limitless AI – Contribute to smarter, more inclusive AI 🌎 Be Part of Us – Join a global, supportive community   About our projects: Welo Data projects vary by scope and complexity, but all play a role in building more accurate, inclusive, and human-centered AI. Depending on your skills, you might: 📝 Annotate or label text, images, or audio to train AI systems 🔍 Evaluate search results, ads, or chatbot responses for quality and relevance 💬 Create and test prompts to improve large language models (LLMs) 🌍 Ensure content reflects cultural, linguistic, or industry-specific accuracy Some projects last a few days; others may extend over several weeks. Before each one, you’ll receive clear details on tasks, timelines, and compensation, so you can decide what works best for you.   How to Apply? Click the “Apply” button on our website to fill out a short form and join our talent database. It only takes a few minutes to get started and become part of our growing community.   📬 Questions? If you have any questions about the process, feel free to contact us at TalentRequests@welocalize.com.