Данные и ML вакансии - удалённо

— Администрировать и поддерживать Percona MySQL PXC Cluster; — Настраивать HAProxy для балансировки нагрузки и высокой доступности;

Location: Europe; New York; Toronto Workplace: Remote 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! Key Overview: As a Member of Technical Staff in Post-Training, you will train our large language models (LLMs) with reinforcement learning and develop methods that help them generalize beyond the distribution they were trained on. You will design the training recipes, environments, and evaluations that tell us whether a model has truly learned a skill or has only fitted its training tasks, and turn what works into the recipes behind our production models. You will work closely with the post-training data infrastructure, evaluation, and pre-training teams. Key Responsibilities - Design, run, and analyse large-scale RL post-training experiments (e.g., RLHF, RL from verifiable rewards). - Research and develop methods that improve out-of-distribution generalization, such as reward design, environment and task diversity, curricula, and regularization. - Build held-out evaluations that measure generalization to unseen tasks, domains, and formats, and that catch reward hacking and benchmark overfitting. - Diagnose training dynamics and failure modes, including instability, reward over-optimization, and capability regressions. - Turn successful research into robust, reproducible training recipes for production models. Qualifications - Strong research and software engineering skills, with hands-on experience training LLMs. - Experience applying reinforcement learning to LLMs (e.g., PPO, GRPO, RLHF, RLVR), ideally at scale. - Track record of work on generalization, robustness, or distribution shift, through publications or shipped models. - Rigorous experimental practice: clean ablations, careful controls, and sound conclusions from noisy results. - Experience with distributed training (e.g., JAX, PyTorch, FSDP, Megatron) is a plus. - Excellent communication skills. - Deep understanding of data quality principles and best practices: demonstrates willingness to dive deep into data while possessing strong intuition for assessing the quality of datasets and anticipating how different data characteristics will impact model training outcomes. 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.

Alpaca is looking for a Senior Data Scientist AI Evaluation to design model evaluations, establish quality standards, and improve AI systems.

Role Focus: Build and deploy production-grade AI/ML and Generative AI systems, with a focus on LLMs, RAG, personalization, MLOps, and cloud infrastructure. - Build RAG pipelines, semantic/vector search, conversational AI, and intelligent assistants.

— Проектирование архитектуры данных: разработка и описание целевой архитектуры хранилищ (OLAP/OLTP), определение моделей данных, выбор форматов хранения и стратегий партиционирования. — Стриминг и батч-обработка: разработка стриминговых джоб на Apache Flink и батч-процессинга на Apache Spark.

— проектировать, разрабатывать и развивать data pipelines; — работать с DWH: моделями данных, слоями, витринами и ETL/ELT-процессами;

🧩 Требования: от 2 лет опыта с Python и библиотеками для работы с данными (pandas, multiprocess, Google Cloud SDK); от 2 лет в ETL/ELT‑технологиях (REST API, GraphQL, DBT) и оркестрации пайплайнов (Airflow, Astronomer, DBT Cloud);

Role Focus: Build and deploy production-grade AI/ML and Generative AI systems, with a focus on LLMs, RAG, personalization, MLOps, and cloud infrastructure. - Build RAG pipelines, semantic/vector search, conversational AI, and intelligent assistants.

Role Focus: Build and deploy production-grade AI/ML and Generative AI systems, with a focus on LLMs, RAG, personalization, MLOps, and cloud infrastructure. - Build RAG pipelines, semantic/vector search, conversational AI, and intelligent assistants.

-Разработка шаблонов потоков данных на основе Flink DataStream. -Реализация архитектуры потоков данных.

• От 3 лет опыта в ML Engineering, Data Engineering или DevOps с развёртыванием и поддержкой ML-систем в продакшене. • Опыт построения ML training pipelines с experiment tracking и model registry (MLflow, W&B или аналоги).

Ищем инженера данных с сильными компетенциями архитектора данных и баз данных, который сможет взять на себя планирование и проведение внедрения инструментов хранения и обработки данных.

• Поэтапный перевод системы с графа сценариев и скиллов на управляемый LLM-based диалог. • Дебаг голосового пайплайна (поиск причин high latency, галлюцинаций LLM, проблем с передачей аудио, графовых ошибок и т.п.).

• Поэтапный перевод системы с графа сценариев и скиллов на управляемый LLM-based диалог. • Дебаг голосового пайплайна (поиск причин high latency, галлюцинаций LLM, проблем с передачей аудио, графовых ошибок и т.п.).

Ищем инженера данных с сильными компетенциями архитектора данных и баз данных, который сможет взять на себя планирование и проведение внедрения инструментов хранения и обработки данных. • Опыт работы на позиции Data Engineer от 4 лет в сфере разработки ПО.

- Разработка Data Pipelines: проектирование и реализация ETL/ELT-пайплайнов: построение DAG в Apache Airflow, написание кастомных операторов и сенсоров на Python, настройка ретраев, алертинга и зависимостей. - Стриминг и батч-обработка: разработка стриминговых джоб на Apache Flink и батч-процессинга на Apache Spark.

Role Focus: Build and deploy production-grade AI/ML and Generative AI systems, with a focus on LLMs, RAG, personalization, MLOps, and cloud infrastructure. - Build RAG pipelines, semantic/vector search, conversational AI, and intelligent assistants.

Responsibilities In this role, you’ll: Build out new functionality within a fast moving product seeing rapid adoption. Take a nebulous idea, design a rigorous experiment, and execute it within weeks.