ML-инженер вакансии

Location: Helsinki; Paris Workplace: Hybrid ABOUT VOODOO Founded in 2013, Voodoo is a tech company that creates mobile games and apps with a mission to entertain the world. Gathering 800 employees, 7 billion downloads, and over 200 million active users, Voodoo is the #3 mobile publisher worldwide in terms of downloads after Google and Meta. Our portfolio includes chart-topping games like Mob Control and Block Jam, alongside popular apps such as BeReal and Wizz. TEAM The Engineering & Data team builds innovative tech products and platforms to support the impressive growth of their gaming and consumer apps which allow Voodoo to stay at the forefront of the mobile gaming industry. The Voodoo Ad-Network is an autonomous product group of around 60 highly driven professionals with an ambitious mission: building top-tier ad network services. Our primary goal is to leverage Voodoo's massive first-party data ecosystem to optimize and scale monetization. We are in a rapid growth phase, expanding into new ventures such as opening to external inventory, penetrating the external advertiser market, and driving social network monetization following our recent acquisition of BeReal. To support this incredible trajectory and promising early results, we are scaling our team. The Models team is a core element of the targeting performance. It leverages machine learning and a strong business understanding to directly impact the product's financial performance. It's composed of mostly senior Data Analysts, Analytics Engineers, Data Engineers and Data Scientists/ML Engineers that iterate together on finding and building the next performing iteration. This role can be either Paris or Helsinki based and done in a hybrid setup. ROLE We're looking for a Senior ML Engineer to join our Models team. You will be joining a dedicated squad of Data Engineers, Data Scientists, and ML Engineers focused on building and maintaining the ML training infrastructure that powers our ad-targeting models from data preprocessing through model deployment to production monitoring. In this role, you will be leading the following topics: - Architectural Ownership: Take end-to-end ownership of highly visible projects from ideation to production release. This includes feature scoping, timeline estimation, architecture design, and benchmarking new technologies. - Pipeline Engineering: Build and maintain quality data and ML pipelines to align with ever-evolving business and machine learning needs. Optimize training pipelines for performance, memory efficiency, and cost (e.g. spot instance strategies, efficient data loading, preprocessed artifact reuse). - Data Scientists Enablement: Enable Data Scientists to iterate faster by providing reusable, well-tested pipeline components (transformers, dataloaders, training utilities) and reviewing their contributions to shared code. Extend dataset capabilities: integrating new data sources, scaling feature windows, and increasing training data volumes without breaking pipeline constraints. - Deep Learning Development: Contribute to deep learning development: GPU workload orchestration, custom PyTorch training loops, and model architecture support. - ML Lifecycle & Reproducibility: Maintain reproducibility and consistency across the ML lifecycle: versioned configs, experiment tracking, and online-offline consistency tooling. - Scalability & Reliability: Collaborate with infrastructure teams on scalability — node pools, resource monitoring, CI/CD migrations. Participate in weekly rotation to triage and resolve alerts from Airflow, dbt, and related systems. - Agile Collaboration: Thrive in a fast-paced agile environment with rapid decision-making processes. You will collaborate daily with back-end developers, data scientists, infrastructure engineers, and product managers. - Mentorship & Team Culture: You will actively contribute to our engineering culture, share knowledge, and ensure every team member feels comfortable, supported, and empowered to grow in their role. PROFILE We are looking for a Senior ML Engineer who deeply understands both the ML training lifecycle and the specific challenges of putting machine learning models into production at scale. - 5+ years minimum of experience as an ML Engineer or a similar role - End-to-End ML Ownership: Problem framing, baselines, experimentation, deployment, and iteration - Python Proficiency: Extensive knowledge for ML pipeline code: preprocessing, training/evaluation workflows, experiment utilities, and reproducible configs - Training Mechanics: Comfortable implementing training mechanics where needed (custom steps/metrics, dataloading patterns, performance-conscious preprocessing), not only notebook-level prototyping - ML Frameworks: Practical experience training and evaluating models with scikit-learn, LightGBM, PyTorch for deep learning - Performance Optimization: Experience optimizing memory usage during data preprocessing and model training - Experiment Management: Hyperparameter tuning, experiment tracking, and reproducible training (configs, seeds, versioning) - Cloud & Infrastructure: Experience with Amazon Web Services. Familiarity with scalability, reliability, and security topics - ML Production Awareness: Understanding of the challenges involved in running ML models in production (familiar with topics such as feature store, training-serving skew, etc.) - Model Serving: Experience with model serving infrastructure (real-time or batch inference, latency/throughput optimization) is a plus - Excellent communication skills in English OUR STACK Python · Scikit-learn · LightGBM · PyTorch · DBT · Spark · MLflow · Prometheus · Terraform · Airflow · Kubernetes · Amazon Web Services BENEFITS - Excellent benefits that will depend on the country you're based in

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: London Workplace: Hybrid 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: Building AI agents that can assist with any kind of enterprise work is a challenging, open-ended problem. One key piece of solving it is replicating real work environments as realistically as possible - filling them with hard tasks to solve, creating plausible input data, and defining clear rewards for completing the work the right way. We build many of these reinforcement learning (RL) environments, then drop our agents into them to evaluate or train them. In this role, you are responsible for creating these RL environments, running AI agents inside them, and improving both the agents and the environments in the process. The results reach customers, whose feedback feeds back in - and the agent/environment improvement loop continues. Key Responsibilities: There are many open problems in this space. As a Member of Technical Staff, RL Environments, you will: - Build new RL environments targeting different agentic capabilities and industry areas - Train and evaluate agents in those environments - Make all the pieces work together: tasks, data, tool implementations, and verifiers - Work across modeling and product to identify gaps in agent performance, and improve both the agents and the environments - Work with external vendors to create high-quality, expert-built RL environments, and build tools to ensure high task, data, and verifier quality - Automate the discovery of model capability gaps, and systematically measure agent performance during evals and training Qualifications: You may be a good fit if: - You have engineered agents and optimized them for specific industry use cases - You have spent dozens of hours reviewing agent trajectories to pinpoint exact failure points and fix them with model training or harness engineering - You obsess over measuring agentic capabilities and turning that into a repeatable process - You have had many debates about what a good outcome from an AI agent should look like, you translated that into verifier implementations and tuned the reward designs - You have designed and run annotation workflows to surface insights into agent performance and verify data quality - You have built synthetic data pipelines to scale eval and training efforts - You use agents yourself in your daily work, and have stories about how you improved your setup to 10x your productivity - A plus: you have experience with training with RL: scaling, troubleshooting and tuning the environments If some of the above doesn’t line up perfectly with your experience, we still encourage you to apply. 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.

Обязанности: • разработка и совершенствование End-to-End ML-пайплайнов • разработка продакшен-пайплайнов обработки данных • работа с огромными объёмами данных Сбера (петабайты) на PySpark, исследование подходов применения их в моделях • писать эффективный и масштабируемый код для тренировки и инференса моделей на PyTorch, проводить эксперименты на GPU-кластере • performance оптимизации кода по обработке больших массивов данных или онлайн сервисов рекомендаций с высокой нагрузкой • менторинг младших членов команды, обмен знаниями и экспертизой. Требования: • математический бэкграунд • хорошее знание Python и ключевых фреймворков для работы с данными (PySpark, PyArrow, Pandas) • опыт написания качественного production кода • опыт написания промышленных пайплайнов обработки данных, содержащих множество шагов, зависимостей и сложную логику • опыт использования Airflow (или другого industry-standard оркестраторов пайплайнов, т.к. Luigi, Dagster и т.д.) • хорошее понимание баз данных SQL / NoSQL • навыки работы с генеративными AI-моделями; опыт создания AI-агентов и использования их в работе будет преимуществом • опыт использования GigaChat, Kandinsky и аналогов в продуктах, навыки. Условия: • комфортный современный офис рядом с м. Кутузовская (опционально) • возможность выбрать удобный график – офис/гибрид/удаленка (опционально) • ежегодный пересмотр зарплаты, квартальная и годовая премия (премия указывается в зависимости от должности и системы премирования) • корпоративный спортзал и зоны отдыха • более 400 образовательных программ СберУниверситета для профессионального и карьерного развития • программа адаптации и помощь руководителя на старте (для вакансий уровня Junior) • расширенный ДМС, льготное страхование для семьи и корпоративная пенсионная программа • 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: -Помощь в развитии рекомендательной системы. -Обеспечение производительности и отказоустойчивости runtime и offline-процессов. -Улучшение ML-инфраструктуры. Требования: -Навыки разработки на Java 21 или более поздних версиях. -Понимание микросервисной архитектуры. -Знание алгоритмов и структур данных. -Опыт работы с Git. -Английский язык на уровне чтения технической документации.

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.

Location: Europe; Canada; New York 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! Role Overview: At Cohere, we're pushing the boundaries of what these technologies can achieve for production environments, and we're seeking a senior engineer who understands the practical challenges of making AI systems work at scale. This role demands deep technical expertise in production environments and the ability to bridge research concepts with real-world implementations. This position is hands-on and engineering-driven. You'll focus on production systems, experiment with frontier models, and collaborate with talented engineers to build scalable, production ready solutions. We strive for excellence in both engineering and science by meticulously designing experiments and design docs. We blend engineering and research seamlessly. Everyone contributes to both, depending on organizational needs. We provide access to world class compute resources, data, and talent to ensure you can do your best work. This role is perfect for you if you: - Have a deep passion for quality work. - Enjoy tuning and optimising large LLM models. - Comfortable working with people with different levels of software engineering skills, from beginner to more advanced. - Comfortable diving into complex ML codebases to identify and resolve issues, ensuring the smooth operation of our systems. - Thrive in a fast-paced, technically challenging environment, where you can contribute your innovative ideas and solutions. Key responsibilities: - Design and write high-performing and scalable software for training models. - Develop new tools to support and accelerate research and LLM training. - Coordinate with other engineering teams (Infrastructure, Efficiency, Serving) and the scientific teams (Agent, Multimodal, Multilingual, etc.). - Research, implement, and experiment with ideas on our cluster and data infrastructure. - Collaborate, Collaborate, and Collaborate with other scientists, engineers, and teams! Qualifications: - Extremely strong software engineering skills. - Value test-driven development methods, clean code, and strive to reduce technical debts at all levels. - Proficiency in Python and related ML frameworks such as JAX, Pytorch and/or XLA/MLIR. - Experience using and debugging large-scale distributed training strategies (memory/speed profiling). - Experience with distributed training infrastructures (Kubernetes) and associated frameworks (Ray). - Experience in ML and LLM academic research. 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.

Задачи: - Развивать инструменты хостинга ML-моделей: внедрять и развивать inference-движки и сопутствующую инфраструктуру, следить за мировыми трендами, делать R&D мировых решений. - Развивать ML-инфраструктуру: трекер экспериментов Clear ML, внутренние инструменты для разметки данных, векторную БД Milvus, решение для запуска распределенных вычислений, а также те инструменты и сервисы, про которые мы уже думали, но еще не внедрили. - Развивать внутренние сервисы: поддерживать планировщик вычислительных ресурсов для запуска DS-решений, бота для глубокого анализа каналов поддержки Центра ИИ и прикладные решения для управления кластерами и биллингом. Требования: - Опыт работы с Docker и Kubernetes для развертывания ML-моделей. - Опыт работы с Ansible. - Знание и опыт внедрения DevOps-практик: CI/CD, IaC, SRE. - Навыки работы с системами мониторинга Prometheus, Grafana, ELK. - Опыт программирования на Python на уровне понимания экосистемы, базовых библиотек и навыков написания прикладных программ. - Опыт проектирования и внедрения инфраструктурных решений. - Способность и готовность работать в условиях высокой неопределенности. Будут плюсами: - Опыт работы в роли MLOps-инженера или аналогичной должности. - Понимание жизненного цикла ML-решений и специфики их развертывания на production. - Опыт работы с векторными БД. - Знание ML-инструментов и библиотек. - Опыт работы с GPU-серверами и диагностики проблем с GPU. Условия: - Уровень дохода, который зависит от ваших технических знаний и навыков. Регулярно пересматриваем зарплаты с учетом профессионального роста. Финансовые бонусы тоже есть. - Заботу о вашем комфорте. Можете работать удаленно, гибридно или в офисе, в любом случае предоставим технику и все необходимое. Если вам по душе работать из офиса — обеспечим удобным местом, зонами отдыха, кухней с полезными перекусами. - Сильное инженерное комьюнити. Регулярно проводим внутренние технические конференции, обмениваемся опытом между командами. Вы всегда найдете, с кем посоветоваться. - Возможность сменить продукт, команду или предметную область внутри компании. Поддерживаем развитие в любом направлении. - Общение на «ты» и максимум горизонтальных связей в коллективе, чтобы быстрее договариваться и решать рабочие задачи. - Возможность делиться экспертизой. Помогаем выступать на конференциях, участвовать в подкастах или видео, писать статьи. - Поддержку профессионального развития. Организуем внутреннее и внешнее обучение.

About our Machine Learning Business Banking team: Our mission in Business Banking is to simplify banking for small businesses; making business banking fairer, simpler and more transparent. Monzo Business Banking is fast-growing, is the UK’s most recommended business account for overall service quality. We recently hit 1 million customers and are used by 1 in 6 UK SMEs. We want to help businesses spend less time on financial admin and more time focused on running and growing their business. We’re building intelligent, data-driven and AI/ML-enabled product experiences that help small businesses feel more in control, make better decisions, and get more value from Monzo. ML is at the core of how we build and scale our products, enabling Monzo to make better decisions, faster, and helping us serve businesses more effectively. As a Lead Machine Learning Scientist in Business Banking, you’ll help build and ship models that power the next generation of Business Banking experiences. You’ll work closely with Product, Engineering, Design, Research, Data Science and Analytics Engineering to bring ML-enabled product capability into the heart of how we build for businesses. Whether it’s helping businesses understand their cash flow, reducing manual admin, surfacing the right insight at the right moment, or helping teams build intelligent product experiences responsibly, our work makes business banking smarter, simpler and more useful for SMEs. What you'll be working on As a Lead Machine Learning Scientist, you’ll be a technical leader and hands-on individual contributor, spearheading our ML and GenAI capabilities and shipping key models that powers magical Business Banking experiences. You’ll: — Be one of the first ML Scientists in Business Banking, bringing leadership through ambiguity by adding structure and direction to our ML capabilities while staying agile and building momentum. — Develop and deploy advanced ML models on our cloud-native data platform to serve hundreds of thousands of business customers. — Partner with Product, data science and other stakeholders to identify the highest-impact opportunities, size impact, and define success metrics. — Decide when to use ML, GenAI, or simpler approaches and be clear on trade-offs. — Design robust evaluation and monitoring so we can ship responsibly and measure impact in production. — Lead the design and implementation of batch and near-real-time models (e.g. LLM-powered experiences, predictive models, time-series forecasting). — Collaborate closely with MLOps and Backend Engineering to operationalise models end-to-end and raise the bar on ML lifecycle rigor. — Set technical standards, mentor others, and support experimentation and iteration. You should apply if: — What we’re doing here at Monzo excites you! — You have a multiple year track record of excellence leading the development and deployment of advanced Machine Learning models to tackle real business problems, preferably in a fast-moving tech company. — You have experience developing and shipping state-of-the-art ML models to production and delivering business impact. — You're impact-driven and excited to own the end-to-end journey that starts with a business problem and ends with your solution having a measurable impact in production. — You have a self-starter mindset; you proactively identify issues and opportunities and tackle them without being told to do so. — You speak Python fluently and have extensive, hands-on experience with scikit-learn. — You are comfortable using SQL, and keen to learn Go, which is used in many of our backend microservices. — You’re comfortable working in a team that deals with ambiguity and have experience helping your team and stakeholders resolve that ambiguity. — You want to be involved in building a product that small businesses use to run and manage their financial lives every day. — You have a product mindset: you care about customer outcomes and you want to make data-informed decisions. — You’re excited about fast-moving developments in Machine Learning and AI and can communicate those ideas to colleagues who are not familiar with the domain. — You’re adaptable, curious and enjoy learning new technologies and ideas. — You’re excited by the opportunity to help Business Banking evolve into AI and ML-enabled product capability. — You can work closely with Product, Engineering, Design, Research and Data colleagues to shape product strategy, clarify trade-offs and build things that customers actually use. — You’re thoughtful about responsible ML/AI; especially in financial products where trust, transparency and customer control matter. Nice to haves: — Experience working on personalisation, ranking, recommendation, forecasting, classification or decisioning problems for customer-facing applications. — Experience working on ML systems for fintech, banking, accounting, payments, lending, invoicing, cash flow, tax, risk or business software. — Experience with GenAI, LLMs, agentic workflows, retrieval, evaluation frameworks, or human-in-the-loop product experiences. — Commercial experience writing critical production code and working with microservices. — Experience designing experiments, A/B tests or other approaches to measure product and customer impact. — Experience working in regulated environments or with products where explainability, auditability and customer trust are especially important. The interview process: Our interview process involves 3 main stages. We promise not to ask you any brain teasers or trick questions! — 30 minute recruiter call — 45 minute call with hiring manager — Full Loop: ML Modelling Interview, Product & ML interview, Behavioural Interview, Leadership interview Our average process takes around 3-4 weeks but we will always work around your availability. You will have the chance to speak to our recruitment team at various points during your process but if you do have any specific questions ahead of this please contact us on tech-hiring@monzo.com. Please also use that email to let us know if there's anything we can do to make your application process easier for you, because of disability, neurodiversity or any other personal reason. What’s in it for you: — We can help you relocate to the UK — We can sponsor visas — This role can be based in our London office, but we're open to distributed working within the UK (with ad hoc meetings in London). — We offer flexible working hours and trust you to work enough hours to do your job well, at times that suit you and your team. — Learning budget of £1,000 a year for training courses and conferences — And much more, see our full list of benefits here If you prefer to work part-time, we'll make this happen whenever we can - whether this is to help you meet other commitments or strike a great work-life balance.

Кого ищем: — Сильного ML-инженера с опытом в трейдинге, который уже имеет опыт разработки прибыльных ML-моделей и торговых стратегий. — Способного самостоятельно генерировать и валидировать торговые идеи, строить исследовательские пайплайны и доводить стратегии до продакшн-состояния. Чем заниматься: — Разрабатывать и улучшать торговые стратегии. — Строить ML и количественные модели для прогнозирования и генерации сигналов. — Анализировать временные ряды и рыночные данные в реальном времени. — Тестировать гипотезы и улучшать существующие стратегии. — Работать с бэктестинговой инфраструктурой. Условия: — Достойная заработная плата в долларах США или криптовалюте. — Удалённый формат работы из любой точки мира, полная занятость. — Оформление через юридическое лицо в Республике Беларусь. — Небольшая команда с минимумом бюрократии, что обеспечивает быстрое принятие решений и оперативную проверку гипотез.

Задачи: - Разработка и обучение ML/DL-моделей для задач NLP и Computer Vision, возможен также classic ML. - Анализ и обработка данных. - Работа с PyTorch. - Тестирование и оптимизация моделей. - Подготовка модели к запуску в продакшене, участие в A/B тестах, поэтапных раскатках и мониторинге качества. - Поддержка существующих моделей. Требования: - Широкий прикладной ML-бэкграунд: опыт работы минимум с двумя направлениями из Computer Vision, NLP / LLM, classic ML (обучение моделей, интеграция их в реальные проекты). - Коммерческий опыт в Data Science / ML от 4 лет. - Уверенное знание Python и PyTorch. - Хорошее знание классического ML и современного DL (включая математику за ними). - Опыт самостоятельного ведения задач на разных этапах проекта. - Умение переводить бизнес-задачу в ML-постановку и предлагать практичные решения.

Задачи: - Разработка и обучение ML/DL-моделей для задач NLP и Computer Vision, возможен также classic ML. - Анализ и обработка данных. - Работа с PyTorch. - Тестирование и оптимизация моделей. - Подготовка модели к запуску в продакшене, участие в A/B тестах, поэтапных раскатках и мониторинге качества. - Поддержка существующих моделей. Требования: - Широкий прикладной ML-бэкграунд: опыт работы минимум с двумя направлениями из Computer Vision, NLP / LLM, classic ML (обучение моделей, интеграция их в реальные проекты). - Коммерческий опыт в Data Science / ML от 4 лет. - Уверенное знание Python и PyTorch. - Хорошее знание классического ML и современного DL (включая математику за ними). - Опыт самостоятельного ведения задач на разных этапах проекта. - Умение переводить бизнес-задачу в ML-постановку и предлагать практичные решения

Задачи: — разрабатывать и внедрять современные решения в области компьютерного зрения Требования: — опыт коммерческой разработки в области Computer Vision от 3 лет — хорошее понимание классического машинного обучения, статистического анализа, классических алгоритмов Computer Vision — практический опыт работы с нейросетевыми фреймворками PyTorch — умение читать научные статьи, воспроизводить решения и адаптировать их под реальные задачи — уверенное владение библиотеками NumPy, scikit-learn, Matplotlib, OpenCV — уверенная работа с Linux (предпочтительно Ubuntu, Mint или Debian) — опыт контейнеризации с использованием Docker и Docker Compose — уверенное владение Git — английский язык — Upper-Intermediate (B2) или выше (обязательно) — наличие проектов, которые были самостоятельно доведены до production Будет преимуществом: — опыт разработки систем видеоаналитики и/или видеонаблюдения — опыт оптимизации скорости инференса моделей с использованием ONNX Runtime и TensorRT — практический опыт работы с Graph Neural Networks (GraphNN/GNN) и Metric Learning — опыт работы с Kubernetes (K8s) Мы ищем человека, который: — любит решать сложные инженерные задачи — следит за современными исследованиями в области AI и Computer Vision — умеет самостоятельно принимать технические решения и доводить проекты до результата — готов работать над задачами полного цикла — от исследования и прототипирования до внедрения в production

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: - Разрабатывать REST API с использованием FastAPI; - Проектировать и реализовать backend-сервисы с нуля; - Проводить интеграции с базами данных и системами обмена данными; - Обеспечивать масштабируемость и отказоустойчивость сервисов; - Писать и поддерживать автоматизированные тесты; - Настраивать и поддерживать CI/CD процессы для непрерывной интеграции; - Участвовать в планировании задач и работе по методологии Scrum; - Тесно взаимодействовать с командой для оперативного решения технических вопросов. Требования: - Сильные инженерные навыки, самостоятельность, понимание полного цикла доставки приложения; - Уверенный опыт настройки и использования CI/CD, работы с Docker и Kubernetes; - Понимание DevOps-культуры, умение написать и поддержать Helm-чарт/Dockerfile, разобраться с деплоем, настроить базовый мониторинг и алертинг; - Глубокие знания Python и опыт работы с FastAPI; - Опыт работы с PostgreSQL, ClickHouse, Kafka, Redis, Spark; - Умение писать чистый, поддерживаемый код с соблюдением best practices и знание архитектурных паттернов; - Опыт работы в высоконагруженных системах и решения реальных задач на production; - Аналитический склад ума, системное мышление и готовность самостоятельно доводить задачи до конца; - Продвинутое знание инструментов мониторинга (Prometheus, Grafana, Opensearch/ELK); - Глубокий опыт в DevOps, опыт работы с Triton Inference Server и другими инструментами деплоя ML-моделей будет плюсом.

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.