ML-инженер вакансии

- Сильный опыт в ML, Deep Learning, NLP и LLM. - Практический опыт создания AI-продуктов, работающих в production и в большом масштабе.

• 8+ лет опыта в прикладном ML/AI с ответственностью за системы end-to-end. • Уверенное владение Python.

• Взаимодействие с data scientists, инженерами, аналитиками и бизнес-стейкхолдерами. • Коммерческий опыт в ML/ML Engineering от 3 лет, уровень Middle+ / Senior.

Предстоит: Проектировать и внедрять ML-решения (от классического регрессионного анализа до Time Series и Uplift-моделей); Строить высокопроизводительные пайплайны обработки данных и генерации признаков на PySpark;

• 5+ лет опыта в Computer Vision и Deep Learning. • Глубокая экспертиза в Python и современных CV-фреймворках.

проектировать и реализовывать end-to-end LLM-пайплайны, включая продвинутые RAG-системы (индексация, ретривал, ранжирование, генерация). разрабатывать автономных AI-агентов с использованием планирования, вызова инструментов (function calling) и мультиагентной оркестрации.

На проекте банковского заказчика предстоит создавать AI-агентов, строить MLOps-инфраструктуру и работать с данными для ML. создавать AI-агенты и платформы для них;

Perplexity is looking for a Machine Learning Engineer to develop search, retrieval, LLM training, and multi-agent systems.

Разрабатывать модели прогнозирования, скоринга, детекции аномалий и AI-агентов. Выводить модели в production и масштабировать их.

About the Team Launched in 2019, Constructor is an AI-first ecommerce search and discovery platform that helps shoppers find the right products at the right time and enables leading global e-commerce brands to drive meaningful revenue and conversion gains. As a member of the Personalization team, you will improve the e-commerce experience for hundreds of millions of users across the world by designing, delivering & maintaining high-load real-time services in close collaboration with great engineers both within the team and across other teams such as the Ranking team. The general goal of the Personalization team is providing best-in-class personalization for all e-commerce experiences provided by Constructor. The job can consist of, but is not limited to: Improve personalized experience across different products, domain verticals and platforms Improve business KPIs by validating hypotheses around personalization Research different personalization approaches to provide the best outcome to the business Model user interactions to provide the best experience to shoppers Collaborate with technical and non-technical business partners to develop and update personalization capabilities Communicate with stakeholders within and outside the team Requirements Hard skills 3+ years of professional experience in applied machine learning Proficiency with Python, SQL and the big data stack for end-to-end ML product development Experience building, shipping, and maintaining personalization / recommender / ranking systems in production Experience with any of the major public cloud platforms will be a plus Experience with design and analysis of A/B tests Great skills delivering & communicating business value to stakeholders Soft skills Excellent English communication skills Experience collaborating in cross-functional teams Ability to deep dive into problems and provide actionable outcomes Ability to work autonomously and independently Proactivity Benefits Work with smart and empathetic people who will help you grow and make a meaningful impact. Regular team offsite events to connect and collaborate. Fully remote team - choose where you live. Unlimited vacation time - we strongly encourage all of our employees take at least 3 weeks per year. Work from home stipend! We want you to have the resources you need to set up your home office. Apple laptops provided for new employees. Training and development budget for every employee, refreshed each year. Maternity & Paternity leave for qualified employees. Base salary: $80k–$120K USD, depending on knowledge, skills, experience, and interview results Stock options - offered in addition to the base salary. Diversity, Equity, and Inclusion at Constructor Constructor.io is committed to cultivating a work environment that is diverse, equitable, and inclusive. As an equal opportunity employer, they welcome individuals of all backgrounds and provide equal opportunities to all applicants regardless of their education, diversity of opinion, race, color, religion, gender, gender expression, sexual orientation, national origin, genetics, disability, age, veteran status or affiliation in any other protected group. They encourage you to apply even if you don’t meet all of their listed qualifications. How they handle your data By submitting this application, you acknowledge that Constructor will collect and process the personal data you provide for the purpose of evaluating your candidacy for this role. If you are not selected, they will retain your data for a limited period as described in their Recruitment Privacy Notice. After that period your data will be deleted or anonymized.

• Опыт применения Generative AI и LLM к задачам discovery.

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.

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.

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.

• 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).

• 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.).