О проекте: ArchOps — архитектурный продукт, позволяющий наполнять и управлять ландшафтом предприятия.
Задачи:
— дорабатывать текущий Copilot продуктового архитектора;
— участвовать в обучении моделей;
— разрабатывать новые AI-агенты.
Требования:
— понимание работы и построения AI агентов;
— базовые навыки Python;
— знание ООП;
— студенты 3 и 6 курсов бакалавриата, специалитета или магистратуры.
Условия:
— график работы 30-40 часов в неделю;
— развитие в командах профессионалов и опытный наставник;
— мобильная связь за счет компании;
— специальные предложения и скидки от продуктов экосистемы МТС;
— собственный корпоративный университет, онлайн-библиотека, приложение Best Benefits с различными скидками;
— спорт: онлайн зарядки, йога, дыхательные практики.
Responsibilities:
• Build AI agents from concept to production, including LangGraph workflows, RAG systems, prompts, tool/function calling, external integrations, and streaming LLM applications.
• Develop backend services with FastAPI/async, PostgreSQL/SQLAlchemy, Celery/Kafka, and write tests; assess quality and resolve production issues.
Requirements:
• Strong Python development and practical LangGraph experience.
• Experience building LLM applications and RAG systems with embeddings, vector search, relevance assessment, Qdrant, and hybrid/dense/sparse search.
• Git, REST, asynchronous backend development, and independent task completion.
• Familiarity with LiteLLM/vLLM and local models such as Qwen or Gemma.
About Us & The Team
Til-Qazyna is a national scientific and practical center dedicated to building the core digital and AI infrastructure for the Kazakh language. We develop foundational language resources, linguistic tools, and open-source models, including custom text corpora, morphological analyzers, RAG pipelines, and fine-tuned Large Language Models.
We are looking for an AI Engineer to focus exclusively on natural language processing, corpus engineering, and LLM fine-tuning.
Key Responsibilities
• LLM Adaptation & Fine-Tuning: Fine-tune, evaluate, and benchmark open-source LLMs (Llama, Mistral, Qwen, Gemma) for Kazakh language understanding and generation using parameter-efficient methods (LoRA, QLoRA, PEFT).
• NLP & Linguistic Tooling: Develop, maintain, and optimize rule-based and neural NLP components, including morphological analyzers, lemmatizers, tokenizers, and terminology parsers.
• Corpus & Data Pipelines: Design automated pipelines for large-scale text crawling, data cleaning, deduplication, synthetic data generation, and dataset curation.
• Inference & Deployment: Package NLP models and LLMs into containerized microservices (Docker, FastAPI) and optimize inference using modern serving frameworks (vLLM, Ollama, TensorRT-LLM, ONNX).
Requirements
• 1+ years of hands-on experience as NLP / AI / Machine Learning engineer.
• Solid understanding of the Transformer architecture, attention mechanisms, embeddings, and tokenization.
• Experience with core ML/NLP libraries: PyTorch, Hugging Face (transformers, datasets, accelerate, peft), Scikit-learn.
• Experience building data preprocessing pipelines and working with structured/unstructured text data.
• Practical knowledge of Docker, Linux, and Git.
• Degree in Computer Science, Data Science, Applied Mathematics.
Nice to Have
• Hands-on experience with modern LLM serving and optimization engines (vLLM, TGI, llama.cpp, quantization techniques like AWQ/GPTQ).
• Experience with RAG stacks (LangChain, LlamaIndex, ChromaDB, Qdrant, FAISS).
• Understanding of the morphological, agglutinative, or grammatical characteristics of the Kazakh language.
• Familiarity with alignment techniques (DPO, RLHF) or synthetic data generation.
What We Offer
• Direct impact on sovereign AI development and language technology used at scale.
• Official employment in full compliance with the Labor Code of the Republic of Kazakhstan.
• Access to high-performance GPU compute clusters for model training and experimentation.
Location: New York, NY; San Francisco, CA
Workplace: OnSite
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 LangSmith Engine team is building a proactive agent engineer that analyzes production traces, identifies important failures, recommends and writes fixes, and helps prevent those issues from coming back. We’re building agents that can understand complex software systems and continuously improve the quality of other AI agents.
ABOUT THE ROLE:
We’re looking for an experienced research engineer to help make the Engine agent more capable and more efficient.
You’ll study real agent failures, build benchmarks that capture what matters, run experiments to improve performance, and turn successful ideas into production. This may include prompting and agent-harness improvements, model selection, fine-tuning and post-training custom models. The focus is on measurable improvements to the overall agent.
This role also requires a understanding of production engineering and system-level tradeoffs. Engine is a production system, so improving an agent is not just about maximizing benchmark performance—it also means understanding the impact on cost, latency, reliability, and scalability. You’ll work in the same team with production engineers to design, test, and ship improvements that work reliably in real-world environments.
Location: SF and NYC
WHAT YOU’LL DO:
- Build and maintain benchmarks and evaluations that measure the quality and efficiency of Engine agents on real-world tasks.
- Design and run experiments to improve agent performance across models, prompting, context, tools, orchestration, and agent strategies.
- Explore and implement post-training and fine-tuning techniques when they can meaningfully improve agent capabilities, quality, or cost.
- Turn successful experiments into production improvements, working closely with engineers and researchers to measure impact and prevent regressions.
- Help define the ML roadmap and technical direction for improving Engine agents, and mentor other engineers through strong technical leadership.
WHAT YOU’LL BRING:
- 4+ years of experience in ML/AI research, or a closely related field.
- Master’s or Ph.D. in a relevant scientific field.
- Hands-on experience working with LLMs and AI agents, including analyzing model behavior and improving real-world performance
- Strong experience designing benchmarks, evaluations, and experiments for AI/ML systems; you know how to tell whether a change actually made an agent better.
- Strong software engineering skills, with a track record of taking ideas from research prototype to measurable production impact.
- You have maximum agency and strong research judgment: you can identify high-impact problems, work through ambiguity, move quickly, and communicate your findings clearly.
NICE TO HAVE:
- Ph.D. in Machine Learning, Computer Science or Physics.
- Hands on experience with LLM-as-a-judge, automated graders, synthetic data generation, or human evaluation.
- Hands on experience with reinforcement learning, preference optimization, SFT, RLHF/RLAIF, or other post-training techniques for LLMs.
- Experience optimizing LLM agents for cost, latency, or task efficiency on productions
- Experience with model serving, inference optimization, distributed systems, or GPU infrastructure.
Compensation
Salary Range: $240,000-$280,000 USD
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:
— разработка backend-сервисы и API на Python
— создание AI-агентов и AI-пайплайны с использованием LangChain, LangGraph или аналогичных инструментов
— работа с LLM: промптами, structured output, tool calling, контекстом и памятью
— участие в создании RAG-систем: загрузке документов, поиске информации и формировании ответов на основе источников
— интеграция решения с базами данных, внутренними API и внешними сервисами
— работа с очередями задач, кэшированием, логированием и мониторингом
— участие в проектировании веб-приложений: продумывать взаимодействие backend, frontend, баз данных, фоновых задач и внешних сервисов
— интеграция backend с пользовательскими интерфейсами и при необходимости внесение базовых изменений во frontend
— написание тестов, исправление ошибок и улучшение качества существующего кода
— участие в обсуждении архитектурных и технических решений
— быстрое создание прототипов, проверка гипотез и доработка решений по обратной связи пользователей.
Требования:
— коммерческий опыт разработки на python (fastapi, django или flask) и уверенное проектирование rest api
— sql и реляционные бд (postgresql), git, docker; понимание клиент-серверной архитектуры и http-стека (авторизация, аутентификация, обработка ошибок)
— базовая архитектура приложений: модульность, разделение ответственности, слои приложения, поддерживаемость кода и взаимодействие сервисов
— синхронная и асинхронная обработка, фоновые задачи, очереди сообщений и брокеры (например, celery с rabbitmq/kafka)
— практический опыт в ai/llm: разработка агентов и чат-ботов, использование langchain/langgraph, управление контекстом llm, оценка качества и защита данных
— rag-системы, векторные базы данных, работа с полуструктурированными данными (pdf, таблицы) через pandas/numpy
— кэширование, логирование и мониторинг (redis, elk/prometheus); базовое знакомство с ci/cd и развёртыванием
— websocket и потоковая передача ответов от llm; базовое понимание frontend (html/css/js, react/vue/next.js) для интеграции интерфейса с api
— использование современных ai-инструментов для ускорения написания кода, тестирования и подготовки документации
Условия:
— офисный формат работы: г. Москва
— ежегодный пересмотр зарплаты и годовая премия
— более 400 образовательных программ в СберУниверситете для развития
— спортзал и зоны отдыха
— расширенный ДМС, льготное страхование для семьи
— гибкий дисконт по ипотечному кредиту, равный 1/3 ключевой ставки ЦБ
— подписка Прайм с возможностью совместного использования на трёх близких
— вознаграждение за рекомендацию друзей в команду Сбера.
Задачи:
— Проектировать и развивать модели компьютерного зрения и видеоаналитики (детекция, трекинг, распознавание лиц, анализ поведения);
— Интегрировать ML-модели в production (backend, edge);
— Обучать и дообучать модели (fine-tuning, transfer learning);
— Работать над реальными AI-проектами с бизнес-применением;
— Сопровождать полный цикл разработки: от исследования до деплоя;
— Оптимизировать модели и повышать качество CV-решений.
Требования:
— Уверенные знания Computer Vision и современных DL-подходов;
— Глубокое понимание машинного обучения и нейронных сетей (CNN, detection/segmentation);
— Уверенное владение Python;
— Опыт работы с OpenCV, NumPy, PyTorch и/или TensorFlow;
— Опыт работы с Git;
— Опыт оптимизации моделей (speed/accuracy trade-offs, inference);
— Опыт решения задач детекции, трекинга и анализа поведения людей.
Условия:
— Формат работы: офис, Астана.
Задачи:
— Разработка ИИ-решений для образовательных систем
— Разработка автоматизированных пайплайнов для проверки и оценки ответов
— Проведение экспериментов с большими языковыми моделями (LLM), промптами и методами дообучения
— Подготовка, обработка и анализ данных
Требования:
— Опыт работы в Data Science, Machine Learning или NLP от 2 лет
— Хорошее знание основ машинного и глубокого обучения
— Уверенное владение Python
— Продвинутый уровень SQL
— Опыт работы с библиотеками для анализа данных и машинного обучения: NumPy, pandas, scikit-learn
Условия:
— Работа в офисе в Астане
Location: New York; San Francisco
Workplace: OnSite
ABOUT US:
AI NEEDS A NEW INFRASTRUCTURE LAYER. WE'RE BUILDING IT AT MODAL.
Every era of computing brought new workloads that previous infrastructure couldn't support: mainframes, databases, and the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice, and the window to build the layer underneath it is open right now.
Our customers include category-defining companies like Ramp https://modal.com/blog/how-ramp-built-a-full-context-background-coding-agent-on-modal, Cognition, DoorDash, and Suno. They rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale.
We've passed $1B in annualized run rate, up 15x from a year ago. In October 2026 we raised a $750M Series D led by Accel at a $15.75B valuation.
Our team includes creators of popular open-source projects (e.g.,Seaborn https://github.com/mwaskom/seaborn,Luigi https://github.com/spotify/luigi), academic researchers, international olympiad medalists, and experienced engineering and product leaders with decades of experience.
THE ROLE:
We are looking for PhD research interns with strong research experience in reinforcement learning, machine learning, and foundation models, including large language and multimodal models, to join our research team.
WHAT YOU’LL WORK ON:
- Improve existing methods and develop new techniques for large-scale model training, optimization, and inference
- Extend models to long-context and long-horizon tasks
- Improve inference-time efficiency, reliability, and robustness in high-stakes real-world deployments
YOU MAY BE A FIT IF:
- You’re currently pursuing a PhD in computer science, machine learning, or a related field
- You have a demonstrated record of research in reinforcement learning, machine learning, foundation models, or related areas
- You have experience developing and evaluating large-scale models or machine learning systems
- You’re familiar with distributed training, large-scale inference, or multi-GPU environments
- You have publications at leading venues such as NeurIPS, ICML, ICLR, CVPR, CoRL, UAI, JMLR, or TMLR
- You have strong programming and engineering skills and can translate research ideas into working implementations
- You have a collaborative, mission-driven mindset and can work effectively across research and engineering teams
Задачи:
— Разработка с нуля мультиязычного голосового ассистента (обязательно: английский + турецкий) для мобильного приложения и личного кабинета клиента.
— Проектирование архитектуры: оценка решений «готовые API vs локальные модели» и последующий вывод open-source LLM в продакшн.
— Интеграция AI-сервисов с текущим бэкендом платформы (основной стек: PHP/Laravel, есть элементы legacy на Yii/C#).
— На перспективу: задачи на стыке Computer Vision (умный процессинг оплат) и телематики (анализ данных с датчиков топлива, предсказание поломок спецтехники).
Требования:
— Отличный Python (коммерческий опыт Middle+ / Senior).
— Понимание архитектуры бэкенда (будет плюсом опыт соприкосновения с PHP/Laravel или Go).
— Опыт разработки голосовых решений (STT/TTS, ASR/NLP), опыт развертывания и кастомизации локальных моделей (LLM) на своих серверах в проде.
— English: уровень B2 и выше (чтение документации, работа с зарубежными AI-ресурсами и проектами).
Условия:
— Локация: Алматы, офис (для сильных кандидатов возможен гибрид или удаленка).
— Занятость: Full-time.
Задачи:
— Развертывать и оптимизировать локальные AI-модели.
— Разрабатывать AI-агентов и RAG-решения.
— Интегрировать AI-сервисы с корпоративными системами.
— Выводить AI-продукты в промышленную эксплуатацию.
Требования:
— Практический опыт локального развертывания LLM.
— Опыт работы с RAG, LoRA/QLoRA, Hugging Face, PyTorch.
— Умение доводить AI-продукты до промышленной эксплуатации.
— Для Strong Middle обязателен подтвержденный опыт самостоятельной разработки и внедрения AI-решений в production.
— Действующее право на трудовую деятельность в Республике Казахстан (оформление разрешений на привлечение иностранной рабочей силы не осуществляется).
Условия:
— Работа в офисе в Астане, full-time.
— Трудовой договор.
— Возможность участвовать в создании собственной AI-платформы компании.
— Зарплата до 2 000 000 тенге net по результатам интервью.
Задачи:
- Управление AI-продуктами от идеи до вывода в прод.
- Участие в проработке AI-стратегии кластера (Roadmap, KPI, приоритизация инициатив).
- Проработка архитектуры AI-решений.
- Координация команд: Data Scientists, ML-инженеры, Data Engineers.
- Решение проблем масштабирования моделей и качества данных.
- Запуск PoC и MVP AI-продуктов.
- Оптимизация инференса моделей (скорость, стоимость, точность).
- Дообучение open-source моделей под задачи бизнеса.
Требования:
- Образование: высшее.
- Опыт работы: от 5 лет на релевантной позиции.
- Опыт в предметной области AI.
- Опыт коммерческой разработки, развитый технический кругозор.
- Навыки презентации для C-level с ценностными тезисами.
- Agile/Scrum для ML-проектов.
- Языки: Python, SQL.
- ML/DS: scikit-learn, TensorFlow/PyTorch, Hugging Face, LangChain.
- MLOps: MLflow, Kubeflow, Airflow, Docker/Kubernetes.
- Данные: Spark, DWH (BigQuery, Snowflake), векторные БД (FAISS, Pinecone).
- Опыт управления коллективом: обязателен.
Требования к личным качествам:
- Лидерство, выстраивание коммуникаций, синхронизация работ, критическое мышление, предпринимательский mindset, адаптивность.
Знание стека:
- LangChain, Kafka, K8s, LLM-модели, NLP-модели — embedding, NER, classification.
Задачи:
— Разработка и улучшение biometric и identity verification решений
— Создание production-level Computer Vision и ML-систем
— Разработка алгоритмов Face Verification, Liveness Detection, Anti-Spoofing и Deepfake Detection
— Оптимизация моделей для высоконагруженных систем
— Работа с большими датасетами и подготовка данных
— Внедрение современных исследований SOTA в production
— Разработка и поддержка полного ML/CV pipeline
— Участие в подготовке решений к международным сертификациям и тестированиям
— Работа над AI-продуктами для банков, финтех-компаний, криптоплатформ, телеком-операторов и государственных организаций
Требования:
— Опыт работы ML Engineer / Computer Vision Engineer от 3 лет
— Опыт работы с Face Biometrics, KYC, Identity Verification или Anti-Fraud решениями
— Глубокие знания Computer Vision и Deep Learning
— Уверенное владение Python
— Опыт работы с PyTorch и/или TensorFlow
— Опыт построения production-grade ML/CV систем
— Опыт работы с large-scale datasets
— Опыт оптимизации inference, latency и производительности моделей
— Понимание архитектуры enterprise biometric systems
— Английский язык на уровне B2
— Обязательное преимущество: Опыт подготовки решений к сертификации iBeta Level 2 или Опыт прохождения / участия в сертификации iBeta Level 2
Будет большим плюсом:
— Опыт работы в biometric, KYC или identity verification компаниях
— Участие в NIST FRVT Evaluations
— Опыт разработки Anti-Spoofing и Liveness Detection систем
— Опыт работы с Deepfake Detection
— Понимание международных biometric compliance и certification standards
— Опыт внедрения SOTA research papers в production
— Опыт teamleading
Условия:
— Работа в международной AI-компании
— Участие в разработке production-grade AI-продуктов для международных рынков
— Официальное трудоустройство по трудовому договору
— График 5/2, 8-часовой рабочий день
— Работа в офисе в Алматы
— Финальный оффер зависит от уровня экспертизы
— Возможность работать над сложными задачами в области Face Biometrics, Digital Identity, KYC, AML, Fraud Prevention и Identity Verification
Location: Canada; Toronto
Workplace: OnSite
WHO ARE WE?
Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems.
We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that.
We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft.
We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us!
Role Overview:
Design and implement novel research ideas, ship state of the art models to production, and maintain deep connections to academia and the government. We have one of the highest ratio of compute to engineers in the world. We do not delineate strongly between engineering and research. Everyone will contribute to writing production code and conducting research depending on individual interest and organizational needs. We have all the compute, data, and talent available for you to do your best work.
Key Responsibilities:
- Design, build and scale agentic AI systems for serving mission critical use cases.
- Research, implement, and experiment with ideas on our supercompute and data infrastructure.
- Learn from and work with the best researchers in the field.
- Execute across the full AI stack and ship products to serve public interest.
Qualifications:
- Canadian citizenship and eligibility for security clearance (required for this role).
- Extremely strong software engineering skills.
- Proficiency in Python and related ML frameworks.
- Experience training, evaluating, and using (as an end-user) LLMs.
- Experience using large-scale distributed (GPU) LLM training strategies.
- Bonus: paper at top-tier venues (such as NeurIPS, ICML, ICLR, AIStats, MLSys, JMLR, AAAI, Nature, COLING, ACL, EMNLP).
Working Location:
This role can be based remotely or from one of our office locations listed on the job description - there is no minimum in-office qualification requirement. We care most about hiring exceptional people regardless of locations, though please check the location listed on the posting for guidance around the core time zone or working hours alignment expected for the role.
FULL-TIME EMPLOYEES AT COHERE ENJOY THESE PERKS:
- A weekly lunch stipend of $75/£75 or equivalent in your local currency for lunch.
- Full health and dental benefits, including a separate budget for mental health.
- RRSP matching, 401K, Pension Scheme.
- 100% Parental Leave top-up for up to 6 months, for either parent.
- Annual enrichment benefits:
Arts & culture, fitness/wellness, quality time, and a workspace improvement credit.
Education & learning stipend for conferences, courses, and coaching.
- 6 weeks of paid vacation (30 working days!)
- Budget for traveling to other offices if you are remote, plus an annual company offsite.
HOW AND WHERE WE WORK:
- Cohere is remote-friendly, but we also have offices in Toronto, London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul with more opening soon.
- For those in the office: a daily lunch program, plenty of snacks, and regular community and social events.
- For those not near an office: a co-working benefit so you can work alongside others in your city.
- Everyone receives a $500 home office stipend to set up your workspace properly.
If any of the above doesn’t line up exactly with your experience, we still encourage you to apply.
We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form https://docs.google.com/forms/d/12a6IrLdF3kI2nonKSr4tiFuz18rLQbaeYV-JM9L4o9Q/edit, and we will work together to meet your needs.
We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider.
Beware of Scams: Cohere will never ask for payment or third-party services (e.g., CV writing) as part of our hiring process. All legitimate roles are listed on the Cohere careers page and LinkedIn only, with all communications from Cohere employees coming from an @cohere.com or @cw.cohere email alias. If jobs are viewed on other sites then please verify these through our official careers https://cohere.com/careers page.
