Вакансии

- Исследование, проектирование и разработка архитектур легковесных ИИ-моделей детекции и сегментации дефектов для работы в offline-режиме на edge-устройствах. - Разработка алгоритмов предобработки изображений (кастомные фильтры для устранения бликов, артефактов освещения и шумов на металле).

Location: Abu Dhabi, United Arab Emirates Workplace: hybrid Autonomous vehicles are coming to the markets in which inDrive serves, but the AV industry has a blind spot: it has almost no data from the cities where we operate. inDrive runs one of the world's largest ride-hailing fleets, concentrated in exactly the emerging markets global AV players have yet to reach. We are building the team that turns that fleet into an AV grade data platform: large scale multi sensor collection from real vehicles on real trips, evaluation of driving models against human drivers, automated and human in the loop causal annotation, and camera first 3D scene reconstruction for simulation. We are hiring the founding leader of this workstream. This is a rare true zero to one: you will hire the team, stand up the vehicles and the data platform, set the technical bar, and own delivery end to end. Key Responsibilities Own the AV data workstream end to end. Roadmap, execution, quality, and outcomes, from vehicle data capture through processing, annotation, and delivery of AV-grade datasets Build the team from zero. Hire and lead a multi disciplinary team covering large scale data engineering, search and serving, ML engineering across divergence detection, edge inference, and VLM based auto labeling, 3D reconstruction research, and data annotation operations. Own the hiring bar and the team's growth Management : Manage ML engineers' charter across the cluster Set technical direction across disciplines. Provide design authority for the full pipeline, including onboard capture, cloud ingestion and processing at fleet scale, semantic and causal labeling, and neural scene reconstruction, and make the key technical decisions on evidence Partner with leaders across the organization. Work closely with the leaders of data platform, ML ops, geo and mapping, and inDrive's wider autonomous vehicle initiatives to align roadmaps, share infrastructure, and unblock delivery Own external partnerships. Drive the technical relationships this work depends on, including the NVIDIA AV ecosystem and prospective AV partners consuming our data Balance research and delivery. Several of the core questions here are open research. You frame milestones as decision gates, redirect work based on evidence, and keep the team shipping measurable results on a schedule Skills, Knowledge and Expertise 10+ years of engineering experience in applied ML, autonomous systems, or large-scale data and ML platforms, including 3+ years in the autonomous vehicle industry or closely adjacent autonomy domains (AV data, simulation, or evaluation platforms, ADAS, robotics perception, HD mapping), and 4+ years managing engineering teams, with a track record of building teams from scratch and delivering from zero to one Strong command of the modern AV and foundation model landscape: vision-language models, multi-modal perception, 3D reconstruction and neural rendering, and the NVIDIA AV ecosystem. Deep enough to set direction, review designs, and make evidence-based technical calls Genuine engineering leadership range: you have successfully led data engineering, platform, or infrastructure delivery, not only ML research, and can evaluate and unblock work across those disciplines Proven ability to take large-scale ML systems from conception to production: data pipelines, training and evaluation infrastructure, and models running under real-world constraints Strong external presence: experience owning technical partnerships, vendor and licensing relationships, or customer-facing technical engagements at a senior level Deep fluency in the modern ML stack (PyTorch or equivalent, large-scale training and inference, Python and Linux) and in cloud data platforms at multi-terabyte scale Bachelor's degree in Computer Science, Engineering, or a related field Nice to have: AV industry experience specifically in offline evaluation, data mining, annotation platforms, or simulation, as distinct from on-vehicle stack development Advanced degree (MS or PhD) in ML, robotics, or computer vision Experience operating in emerging markets, and comfort building where the playbook does not exist yet Why join us Help us challenge injustice by creating fair choices for millions of people across 47 countries Develop your professional skills with access to mentoring, career consulting, and learning programs. Collaborate with teams around the world and gain international experience through our Global Talent Exchange Program Engage in company-wide challenges, awards, sports activities, employee-led social impact and volunteering projects Work alongside people who take initiative, speak openly, and challenge themselves to grow Improve your language skills through co-financed courses and internal speaking clubs Final benefits may vary depending on the location.

Agile Robots SE is looking for a DevOps Engineer with AI ML Evaluation Focus to operate cloud infrastructure, automate deployments, and evaluate AI models.

Мы ищем Python-разработчика с опытом от 3 лет для создания backend-сервисов, интеграции AI-агентов и RAG-систем в бизнес-процессы.

Location: Remote - United States Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . Who We Are: The Machine Learning Platform team at Reddit is a high-impact team that owns the infrastructure that powers recommendations, content discovery, user and content quantification, while directly impacting other teams such as Growth, Ads, Feeds, and Core Machine Learning teams. What You’ll Do: As a Senior Software Engineer, ML Platform, you will work on our ML Data Team and be instrumental in architecting, implementing, and maintaining foundational ML infrastructure powering Feeds Ranking, Content Understanding, Recommendations, Ads, and much more to fulfill Reddit’s mission of bringing community and belonging to everyone in the world. You will create ML data-governance tools that help MLEs discover and reason about signals, while contributing across the broader ML data platform—including prediction logging and training data systems. Lead the design, development, testing, and operation of ML signal-governance capabilities that help MLEs discover signals and understand their ownership, lineage, quality, and usage. Build tools that help teams assess signal importance and make informed lifecycle decisions. Partner with MLEs and cross-functional teams to define governance workflows and deliver reliable, intuitive ways to explore and act on signal metadata. Contribute across the ML Data Platform, including prediction logging and training data platforms, to design, develop, test, and operate batch and streaming pipelines processing petabyte-scale data. Drive complex projects end to end from technical planning and design through implementation, testing, rollout, and ongoing operation. Who You Might Be: 5+ years of experience in the ML Platform domain. Strong customer focus and experience driving projects from requirements to production in close collaboration with multiple cross-functional partners (XFN). Experience with large-scale batch and streaming data processing. Experience building production-quality systems with testing, monitoring, observability, and reliability practices. Deep understanding of distributed systems and data-intensive architectures, with strong communication and collaboration skills. Benefits: Comprehensive Healthcare Benefits and Income Replacement Programs 401k with Employer Match Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support Family Planning Support Gender-Affirming Care Mental Health & Coaching Benefits Flexible Vacation & Paid Volunteer Time Off Generous Paid Parental Leave #LI-Remote Pay Transparency: This job posting may span more than one career level. In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/ . To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below. The base salary range for this position is: $216,700 — $303,400 USD In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews. During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable.  We will not sell your personal information or disclose it to any third party for their marketing purposes.  We will delete any recording of your interview promptly after making a hiring decision.  For more information about how we will handle your personal information, including our retention of it, please refer to our Candidate Privacy Policy for Potential Employees and Contractors . Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve.  Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.

Задачи: — строить и эксплуатировать масштабируемые сервисы инференса и production API. Требования: — опыт работы с машинным обучением, GPU, профилированием, инференсом, батчингом, API, контейнеризацией, Python, компьютерным зрением.

Проект NormalDance Protocol — слой идентификации музыки и автоматического распределения роялти по цепочке авторов: перцептивный аудиоотпечаток ADNA, реестр прав, выплаты через СБП. Прототип работает, прод-пилот идёт на действующей музыкальной площадке. Грант ФСИ «Старт-1» (НИОКР 12 мес). Задачи: — аттестационный стенд идентификации — выборка ≥1000 треков / ≥5 жанров, робастность к форматам, битрейтам, питч-сдвигам; — подтверждение операционных порогов статистикой, а не «на трёх файлах»; — матчинг фрагментов (сэмпл внутри чужого трека, обрезки, питч); — воспроизводимый аудио-корпус и регрессии. Условия: — 112 000 ₽ gross/мес (~97 000 на руки), 12 мес, полная занятость, — полностью удалённо из любого города РФ. — Вилка — грантовые рамки ФСИ; — за досрочные результаты — бонусы и долевое участие от компании. — Оформление — ТД с компанией-получателем гранта, выход в проект с первого месяца НИОКР. Требования: — умение строить честные измерения: методика, выборки, цена ошибок первого/второго рода; — опыт с аудио/DSP/индексами — плюс. Детали и техпостановка — под NDA.

Задачи: — Работа с геоданными: геокодирование, map matching, построение изохронов, расчёт ETA — Проектирование и оптимизация графов дорожной сети, работа с роутинг-движками (OSRM, GraphHopper, Valhalla или аналогами) — Анализ и улучшение метрик качества маршрутизации: точность ETA, эффективность маршрутов, загрузка транспорта / курьеров

Задачи: — разработка multi-agent систем (MAS) для Deep Research задач — создание LLM-агентов с использованием tool calling, planning и reasoning — проектирование и развитие RAG-архитектуры включая поверх коллекции из 850+ млн документов — разработка и поддержка knowledge graph для аналитических и исследовательских сценариев — оптимизация LLM inference (latency, throughput, batching, KV cache) — подготовка и оптимизация моделей для production — разработка воспроизводимых ML pipelines и инфраструктуры экспериментов — внедрение и эксплуатация AI / ML сервисов в production Требования: — 3+ лет опыта реализации проектов в Machine Learning / AI — знание паттерной архитектуры и системного дизайна — уверенное владение Python — опыт разработки систем на базе LLM — опыт построения RAG систем или AI-агентов — опыт вывода ML/AI сервисов в production — понимание архитектуры ML pipelines и инженерного подхода Условия: — участие в развитии одной из крупнейших аналитических платформ научных данных — работа с современными LLM и AI-архитектурами — сильная исследовательская и инженерная команда — гибкий график (начало рабочего дня с 8:00 до 11:00) — гибридный формат работы (возможна удалёнка) — обучение и курсы НИУ ВШЭ, английский язык — доступ к библиотеке НИУ ВШЭ и образовательным ресурсам — софинансирование ДМС — программы лояльности фитнес-клубов и театров — офис на Мясницкой (м. Лубянка / м. Тургеневская)

Location: New York City Office Workplace: OnSite COMPANY DESCRIPTION Today, when you go to your doctor and get referred to a specialist, your doctor sends out a referral and tells you, “They’ll be in touch soon.” So you wait. And wait. Sometimes days, weeks, or even months. Why? Because too often providers are overwhelmed with the painstakingly tedious work required to get paid by insurance companies. Powered by proprietary models, Tennr handles the complex paperwork that gets patients through the door and providers paid, helping operators get patients the right care, at the right time, in the right setting. ROLE DESCRIPTION We’re looking for a high-agency Senior Backend Engineer to bridge the gap between cutting-edge ML research and real-world product delivery. You’ll design and build agentic workflows that power complex healthcare processes to solve hairy infrastructure, workflow, and UX challenges in U.S. healthcare. This role involves creating data pipelines, evaluation harnesses, and scalable production-grade agentic systems. You’ll be responsible for researching the right tools and models, ensuring we always use the best-fit technology for each workflow.. As part of our ML team, you’ll work hand-in-hand with researchers to translate new techniques and insights into production-ready systems. The ideal candidate has full-stack engineering experience, hands-on expertise with LLM APIs, and a proven track record of deploying AI systems into production. You’ll work across the stack - from data collection to training pipelines to AI agent orchestration to frontend integration - bringing cutting-edge AI solutions to life in real-world healthcare environments. RESPONSIBILITIES As a Senior Backend Engineer on the ML team at Tennr, you’ll bridge the gap between cutting-edge ML research and real-world product delivery. You’ll help build agentic systems that use LLMs, and vision models, and structured automation to solve hairy infrastructure, workflow, and UX challenges in U.S. healthcare. - Ship full-stack AI systems end-to-end - Design resilient systems for model deployment, evaluation, and monitoring that stay reliable as traffic grows. - Go from ideation to code within hours; iterate quickly on experiments and data. - Collaborate with ML engineers, backend engineers, and cross-functional teams to integrate models cleanly with data pipelines and product QUALIFICATIONS - Proficiency in full-stack development, with a strong understanding of system design, backend engineering, and observability infrastructure. - A track record of working through the full lifecycle of building, testing, deploying, scaling, and monitoring LLM-centered software architectures. - Excellent communication and collaboration skills: aligning on interfaces, navigating tradeoffs, and driving cross-team execution. WHY TENNR? - Drive Impact: one of our company values is Cowboy, meaning you set the pace. You won’t just talk about things, you’ll get them done. And feel the impact. - Develop Operational Expertise: learn the inner workings of scaling systems, tools, and infrastructure - Innovate with Purpose: we’re not just doing this for fun (although we do have a lot of fun). At Tennr, you’ll join a high-caliber team maniacally focused on reducing patient delays across the U.S. healthcare system. - Build Relationships: collaborate and connect with like-minded, driven individuals in our Hudson Square office 4 days/week - Free lunch! Plus a pantry full of snacks. BENEFITS - Beautiful new office at 345 Hudson Street - Unlimited PTO - 100% paid employee health benefit options - Employer-funded 401(k) match - Competitive parental leave

Задачи: — Разрабатывать Python / SQL-пайплайны загрузки и обработки данных из файлов, API и информационных систем. — Проектировать таблицы хранилища: сущности, связи, правила преобразования. — Встраивать псевдоанонимизацию и проверки качества в потоки данных. — Готовить признаки и версионируемые train/validation/test-выборки. — Обеспечивать прослеживаемость и воспроизводимость ML / LLM-экспериментов. — Вместе с ML-командой развивать оценку моделей, подготовку данных для RAG и дообучения. Требования: — Уверенные Python и SQL, поддерживаемый код за пределами ноутбуков. — Практический опыт ETL / ELT: оркестрация, инкрементальная обработка, повторные запуски. — Опыт с S3, Parquet, pandas / Polars / PyArrow. — Понимание жизненного цикла ML: выборки, baseline, метрики, проверка утечек. — Опыт с MLflow, ClearML или аналогами. — Linux, Git, Docker, CI/CD. — Умение работать с чувствительными данными. Условия: — Занятость: full-time — Формат работы: гибридный / удаленный по РФ

Мы в AnySense создаем AI-систему для автоматизацию административных процессов в американском healthcare секторе (с фокусом на автоматизацию работы страховых на US рынке). Задачи: — Разработка и оптимизация Python-бэкенда — Архитектура микросервисов и API (LLM, OCR/CV, клиент и админки) — Создание пайплайнов для LLM-агентов — Интеграция и поддержка OCR и CV-модулей — Тюнинг LLM, работа с датасетами и метриками в проде — Автоматизация процессов через RPA — Поддержка CI/CD и DevOps-процессов Требования: — Понимание архитектуры, системного дизайна и API — Уверенный опыт с Python (4+ лет) — Опыт с LLM-агентами, OCR/CV или похожими AI-модулями — Prompt engineering, тюнинг LLM и датасеты — Понимание / применение RPA — Опыт с CI/CD, Docker, продакшен-развёрткой Бонус: опыт в healthcare и AI-комплаенсе Условия: — USD 6000-10000 / мес — Remote (timezone +-2-3 часа от CET) — Офлайн-встречи несколько раз в год (EU, другие локации) — Работа в core team c founder’ом и СTO.

В рамках данной позиции предстоит заниматься проектированием и разработкой AI-агентов, включая реализацию сценариев, оркестрацию, вызов инструментов и интеграцию с внутренними сервисами. Необходимо преобразовывать прототипы и модели Data Science в стабильные промышленные решения, обеспечивая их упаковку, интеграцию в сервисы и пайплайны, версионирование и сопровождение. Работа включает разработку на Python сервисов, библиотек и пайплайнов, написание автотестов и участие в код-ревью. Также требуется проектировать, создавать и поддерживать витрины данных для ML-моделей и агентов, обрабатывать большие объемы данных с использованием SQL, Hadoop и Spark, оптимизировать запросы и вычислительные задачи. Важной частью является обеспечение качества и наблюдаемости решений через логирование, мониторинг, обработку ошибок и контроль качества данных. Кандидат должен иметь опыт промышленной разработки на Python не менее двух лет, уметь писать поддерживаемый код с типизацией, тестированием, обработкой ошибок и логированием, а также уверенно владеть SQL, включая сложные запросы, оконные функции и оптимизацию. Требуется опыт построения и сопровождения ETL/ELT-пайплайнов и витрин данных, а также опыт вывода сервисов, моделей или data-продуктов в промышленную эксплуатацию. Необходимо понимание принципов работы AI-агентов, включая оркестрацию, вызов инструментов, управление состоянием и интеграцию с API, а также жизненного цикла ML-модели от артефакта и данных до инференса, мониторинга и обновления. Будет преимуществом опыт разработки AI-агентов с использованием LangGraph, LangChain, LlamaIndex или собственного оркестратора, а также опыт работы с LLM, RAG и векторными хранилищами. Дополнительным плюсом считается опыт оркестрации пайплайнов в Airflow или аналогичных системах, работы с Kafka и событийными архитектурами, знание Docker, CI/CD и Kubernetes, практический опыт с Hadoop и Spark, внедрение ML- или LLM-сервисов во внутреннем контуре, а также опыт в банковском домене, продуктах для юридических лиц или документообороте. Формат работы — офис или гибрид в Москве.

Location: San Francisco, CA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role: The Safeguards ML Inference Path team designs, builds, and operates the production infrastructure that powers Claude's ML based safety systems. We collaborate closely with safety researchers and inference engineers to bring new classifiers and novel classes of ML defenses to production. We own the research → production transfer of new safety technologies that is on the critical path for every Claude model launch. And we build for scale: serving thousands of ML classifiers, for all requests on the token generation path, and for every platform Claude runs on -- 1P, Bedrock, Vertex, and beyond. We’re growing the team and looking for engineers who have deep expertise in productionizing ML systems. You'll work at the intersection of machine learning, large-scale distributed systems, and AI safety, developing the platforms and tools that enable our safeguards to operate reliably at scale. And your tooling and infrastructure will be used for every model launch, which are becoming more complex, and more frequent. Responsibilities: Design and build scalable ML infrastructure to support real-time safety deployments across our classifier and model ecosystem Build monitoring and observability tools to track classifier performance, data quality, and system health for safety-critical applications Collaborate with research teams to productionize safety research, translating experimental safety techniques into robust, scalable systems Optimize inference latency and throughput for real-time safety evaluations while maintaining high reliability standards Implement automated testing, deployment, and rollback systems for ML models in production safety applications Partner with Safeguards, Security, and Alignment teams to understand requirements and deliver infrastructure that meets safety and production needs Contribute to the development of internal tools and frameworks that accelerate safety research and deployment You may be a good fit if you: Are proficient in Python and have experience with ML frameworks like PyTorch, TensorFlow, or JAX Understand distributed systems principles and have built systems that handle high-throughput, low-latency workloads Have built automated or self-service deployment pipelines and eval infrastructure allowing researchers to roll out classifiers and models independently Have implemented A/B testing frameworks and experimentation infrastructure for ML systems Are results-oriented, with a bias towards reliability and impact in safety-critical systems Enjoy collaborating with researchers and translating cutting-edge research into production systems Care deeply about AI safety and the societal impacts of your work Strong candidates may also have experience with: Have 5+ years of experience building production ML infrastructure, ideally in safety-critical domains like fraud detection, content moderation, or risk assessment Working with large language models and modern transformer architectures Developing monitoring and alerting systems for ML model performance and data drift Experience in trust & safety, fraud prevention, or content moderation domains Knowledge of privacy-preserving ML techniques and compliance requirements The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: San Francisco, CA About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. About the role: The Safeguards ML Sampling Path team builds and operates the production services that power Claude's safety systems. These services sit on the token generation path across every platform Claude runs on: every request must pass through them, and each millisecond of added latency is wait time for our users. You’ll keep p99 latency flat as traffic grows, build for robustness as dependencies time out or partially fail, and ship changes safely to a system that cannot go down. What you'll do: Design, build, and operate the backend systems that process every token on the generation path for Claude requests, including the streaming contract with the API and inference engines. Own latency and reliability end to end: define and maintain SLOs and error budgets for added latency, time-to-first-token, and availability, and lead incident response and postmortem follow-through. Ship changes to the hot path rapidly but safely — canaried and gradual rollouts, error budget and latency gating, fast rollbacks — and drive per-token performance: chase tail latency and keep cost flat as traffic, models, and checks per request grow. Set technical direction for the sampling path: lead design reviews, make latency, reliability, and cost trade-off calls with the inference and research teams, mentor engineers, and raise the operational bar for the wider Safeguards organization. You may be a good fit if you: Have designed, built, and operated high QPS systems at global scale, and were accountable for them in production: incident response, outages, and postmortem-driven remediation. Have a strong foundation in distributed systems: replication, consistency tradeoffs, failure modes, and SLO management under load. Design systems for graceful degradation: you plan for a slow dependency, a dropped stream, or a half-rolled-out deploy before it happens, and build so the system degrades predictably instead of failing. Have successfully shipped broad or all-encompassing changes to mission critical systems (e.g., database migrations, interface changes, rewrites). Strong candidates may also have: 8+ years of industry software engineering experience. Familiarity with LLM inference systems and transformer-based models (not required, but a plus). The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. Annual Salary: $320,000 — $485,000 USD Logistics Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. For roles performing work in California: We will consider for employment all qualified applicants, including those with arrest or conviction records or criminal histories, in a manner consistent with the requirements of applicable state and local laws, including the San Francisco Fair Chance Ordinance, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings. How we're different We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills. The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences. Come work with us! Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.

Location: Remote - United States Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com . Reddit has a flexible workforce! If you happen to live close to one of our physical office locations our doors are open for you to come into the office as often as you'd like. Don't live near one of our offices? No worries: You can apply to work remotely in any country in which we have a physical presence About Reddit Reddit is a community of communities, built on shared interests, passion, and trust. With 100,000+ active communities and 101M+ daily active unique visitors, we’re one of the largest sources of conversation and knowledge on the internet. For more information, visit redditinc.com . Team Overview The Ads ML Platform team builds infrastructure that accelerates high-scale ML systems and tooling for Ads ML, while extending reusable capabilities to broader Reddit ML use cases where appropriate. Our systems help ML engineers move faster across the full development lifecycle: creating features, generating training data, running offline experiments, validating model quality, launching production models, and operating ML systems reliably. We are looking for a Senior Staff Machine Learning Systems Engineer to lead the technical strategy for the end-to-end Ads ML engineer lifecycle. The initial focus will be on the feature development and training iteration loop: making it faster and easier for ML engineers to build features, generate reliable training data, run experiments, and move from idea to validated model improvement. Over time, this scope will expand into serving and online experimentation workflows, creating a more seamless path from offline iteration to production impact. This is a senior technical leadership role for someone who can combine deep systems expertise, production ML experience, architectural judgment, and cross-team influence. What You’ll Do Own the technical strategy for the end-to-end Ads ML engineer lifecycle, starting with feature development, training data, offline experimentation, and model iteration workflows. Align Ads ML platform priorities with Reddit’s broader ML Platform vision, translating Ads pain points into reusable platform capabilities where appropriate. Define architecture and technical standards for ML feature and training-data systems across batch/streaming computation, backfills, lineage, quality, observability, and online/offline consistency. Stay close to ML engineers and platform customers to identify high-leverage friction points and improve day-to-day development velocity. Build platform abstractions and workflow automation that make ML development faster, safer, more reliable, and more self-service. Over time, extend the platform strategy into serving and online experimentation workflows, creating a more seamless offline-to-online ML development experience. Partner across Ads, ML Platform, Data Platform, modeling, product, and engineering teams to clarify ownership, resolve ambiguity, and drive durable execution. Mentor Staff and senior engineers, raise the architecture and operational bar, and help grow the next generation of technical leaders. Who You Might Be You have 8+ years of experience in infrastructure, distributed systems, ML platforms, data platforms, or large-scale backend systems. You have 4+ years building or operating production ML infrastructure, feature platforms, training data systems, experimentation systems, or large-scale data pipelines. You have led broad, ambiguous, multi-team platform initiatives from strategy through adoption. You have built platforms used directly by ML engineers, data scientists, or product teams developing production ML systems. You have deep experience in ML platform, feature platform, training data, experimentation, developer infrastructure, or distributed data infrastructure. You have worked with distributed data and compute systems such as Spark, Flink, Kafka, Ray, Airflow, Iceberg, Kubernetes, BigQuery, Snowflake, Databricks, or similar technologies. You can balance urgent customer needs with durable long-term architecture and reusable platform patterns. You influence senior engineers and leaders through clear technical reasoning, RFCs, design reviews, decision frameworks, and operating mechanisms. You are excited to shape how production ML systems are built, scaled, and operated, not only how models are trained. Benefits: Comprehensive Healthcare Benefits and Income Replacement Programs 401k with Employer Match Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support Family Planning Support Gender-Affirming Care Mental Health & Coaching Benefits Flexible Vacation & Paid Volunteer Time Off Generous Paid Parental Leave Pay Transparency: This job posting may span more than one career level. In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/ . To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, and may vary from the amounts listed below. The base salary range for this position is: $292,500 — $409,500 USD In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews. During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable.  We will not sell your personal information or disclose it to any third party for their marketing purposes.  We will delete any recording of your interview promptly after making a hiring decision.  For more information about how we will handle your personal information, including our retention of it, please refer to our Candidate Privacy Policy for Potential Employees and Contractors . Reddit is proud to be an equal opportunity employer, and is committed to building a workforce representative of the diverse communities we serve.  Reddit is committed to providing reasonable accommodations for qualified individuals with disabilities and disabled veterans in our job application procedures. If, due to a disability, you need an accommodation during the interview process, please let your recruiter know.

Location: Paris; Amsterdam; Munich; London ABOUT MISTRAL Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited. ABOUT THE JOB   Mistral AI is seeking an Applied AI Engineer focused on DevOps to facilitate the adoption of its products among customers and collaborate with them to address complex technical challenges.   Applied AI Engineers, ML Infra at Mistral AI work directly with customers to quickly understand their greatest challenges and design and implement AI solutions. In this role, you’ll apply your problem-solving ability, creativity, and technical skills to help organizations use AI to drive real impact in the world. You’ll have the opportunity to gain rare insight into and contribute to some of the most important industries and institutions globally!   Applied AI Engineers, ML Infra responsibilities look similar to those of a startup CTO: you’ll work in small teams and own end-to-end execution of high-stakes projects. A day might span discussing architecture with fellow engineers, wrangling massive-scale data, coding a custom web app, speaking with customer executives, or establishing a strategy for the Applied Engineering team.   WHAT YOU WILL DO   • You’ll be responsible for onboarding customers on our products, providing guidance on deployment and integration, and ensuring the best production setup from the low-level GPU stack up to infrastructure, back-end and front-end interfaces. • You’ll work on deploying state-of-the-art AI applications from consumer products to industrial use cases, driving with our customers a crucial technological transformation. • You’ll work in collaboration with our researchers, other AI engineers, and product engineers on our most complex customer projects involving deployment, scaling, and contributing to our open-source codebases for tasks such as inference and fine-tuning. • You’ll be involved in pre-sales calls to understand potential clients' needs, challenges, and aspirations. You will provide technical guidance on our products and explain Mistral technologies to various stakeholders.   ABOUT YOU   • You are fluent in English • You hold a Bachelor's or Master's degree in Computer Science, Engineering, or a related field • You have 2+ years of experience in a DevOps or Site Reliability Engineering role • You're experienced with deploying and managing AI-based products in production environments • You are fluent in Python • You have experience with containerization technologies such as Docker and Kubernetes • You have experience with CI/CD pipelines and automated deployment tools • You have deep understanding of cloud platforms (AWS, Azure, GCP) and on-premises infrastructure • You are experienced with infrastructure as code (IaC) tools such as Terraform or Ansible • You hold strong communication skills with an ability to explain complex technical concepts in simple terms to technical and non-technical audiences IDEALLY YOU HAVE: • Experience as a Customer Engineer, Forward Deployed Engineer, Sales Engineer, Solutions Architect, or Technical Product Manager • Familiarity with AI frameworks such as PyTorch or TensorFlow • Contributions to open-source projects, particularly in the space of DevOps or AI     By applying, you agree to our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy. WHAT WE OFFER We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks. For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link. PRIVACY POLICY Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

Задачи: — Реализация продвинутых оптимизаций инференса (спекулятивное декодирование, квантизация, модификации внимания и др.) для максимизации mining weight. — Настройка Docker-конфигураций для различных моделей GPU на основе доступного реестра. — Разработка пользовательских стратегий оптимизации, балансирующих пропускную способность и качество. — Создание и поддержка пользовательских Docker-образов, оптимизированных под конкретные архитектуры GPU. — Проектирование и внедрение систем для стабильного и масштабируемого майнинга Gonka и других протоколов. — Разработка оптимизированных образов для Tenstorrent AI ASICs для расширения аппаратной экосистемы. — Миграция Python-кода и реализаций VLLM на новые VLLM-образы с адаптацией под конкретные GPU. Требования: — Подтвержденный опыт оптимизации больших языковых моделей. — Глубокое понимание архитектур трансформеров и механизмов внимания. — Владение PyTorch, CUDA и методами оптимизации GPU. — Опыт работы с vLLM, FlashInfer или аналогичными фреймворками оптимизации инференса. — Знание Docker и управления GPU-нагрузками. Желательные квалификации: — Опыт работы с Claude Code Max (предоставляется при необходимости). — Опыт работы с Gonka или аналогичными децентрализованными AI-сетями. — Опыт в конкурентной оптимизации ML или распределенных систем. — Знание архитектур NVIDIA GPU (B200/B300/H200/H100/A100). — Знание Tenstorrent AI ASICs или специализированного AI-оборудования. Условия: — Возможность работы с передовой AI-инфраструктурой. — Бонусы, зависящие от достигнутых улучшений mining weight. — Возможность постоянной работы с долей в прибыли от майнинга. — Гибкий удаленный режим работы. — Доступ к высокопроизводительному GPU-оборудованию для экспериментов.