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Senior Machine Learning Research Engineer (Python)

Here at Humanoid, we believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. We’re bringing that mission to life wi…

humanoid

Зарплата не указанаВеликобританияLondon

Here at Humanoid, we believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. We’re bringing that mission to life with HMND‑01 - our rapidly developed humanoid platform being deployed in real industrial environments - and we’re growing the team to take it even further.

About the Role

We are looking for a strong Machine Learning Research Engineer to join our Autonomy team and develop the learning-based algorithms that power dynamic locomotion and whole-body control on our humanoid robots.

You will develop robust, reusable motion capabilities that enable the robot to move dynamically, maintain balance, interact with its environment, and combine locomotion with manipulation. This includes building the underlying learning-based motion primitives that can be composed into increasingly complex whole-body behaviors.

You will work closely with robotics, controls, simulation, and machine learning engineers to turn learned behaviors into reliable capabilities running as part of the robot's real-time control stack.

 

What You'll Do

  • Design and train learning-based locomotion and whole-body control policies for dynamic robotic behaviors.

  • Develop motion primitives using different learning based generative models.

  • Apply reinforcement learning, imitation learning, and other learning-based approaches to motion generation and control.

  • Build scalable simulation, data generation, and training pipelines using platforms such as Isaac Lab and MuJoCo.

  • Develop methods for robustness, adaptation, and sim-to-real transfer, including domain randomization and learning from real-world robot data.

  • Deploy, evaluate, debug, and fine-tune learned policies on physical humanoid robots.

  • Optimize policy inference and training systems for the performance and latency requirements of real-time control.

  • Integrate learned policies into the broader robot control and autonomy stack.

 

What We're Looking For

  • BS, MS, or PhD in Robotics, Machine Learning, Computer Science, or a related technical field.

  • 3+ years of experience developing machine learning systems in industry or research.

  • Strong understanding of deep learning and modern policy architectures, including training, inference, experimentation, and debugging.

  • Hands-on experience with RL and at least one additional area such as imitation learning, generative models, sequence models, or large-scale representation learning.

  • Strong Python and PyTorch experience.

  • Experience with physics-based simulation environments such as Isaac Lab, Isaac Sim, MuJoCo, or similar platforms.

  • Ability to reason about learning algorithms in the context of dynamics, control, and physical systems.

Nice to have:

  • Experience applying reinforcement learning or imitation learning to legged robots, humanoids, or other complex physical systems.

  • Experience with whole-body control, locomotion, motion imitation, or loco-manipulation.

  • Experience deploying learned policies on real robotic systems and debugging the sim-to-real gap.

  • Familiarity with motion capture, trajectory optimization, model-based control, or learning from demonstrations.

  • Experience training policies at scale across large numbers of simulated environments.

  • Publications at NeurIPS, ICML, ICLR, CoRL, RSS, CVPR, or equivalent contributions to impactful open-source robotics or machine learning projects.

 

What We Offer

  • Competitive equity: stock options with meaningful upside as we scale.

  • 30+ paid days off, including 23 days of annual leave, all UK bank holidays, and additional company closure days (including Christmas–New Year shutdown).

  • Private healthcare, including virtual and in-person care.

  • Pension scheme with 8% total contribution (5% employee, 3% employer) on full earnings.

  • Free daily breakfast, catered lunch, and snacks in-office.

  • Work at the frontier - collaborate daily with world-class engineers, researchers, and product experts building the next generation of AI and humanoid robotics.

  • Real ownership - direct access to founding leadership, meaningful input on product direction, and the ability to drive key initiatives from day one.

Похожие вакансии Data Science & ML

hh.ru
navio

Инженер в области глубокого обучения в команду ML Планера

navioАвтоподнятие
Зарплата не указанаРоссияМоскваГибрид

О команде:

Планирование движения автономного автомобиля и предсказание движения всех участников вокруг него - это сложная и крайне важная задача, учитывающая множество факторов. Качественно планируя и предугадывая будущее такой автомобиль сможет обеспечить комфорт пассажиров и избежать потенциальных коллизий.

Мы ищем опытного Специалиста с глубокими знаниями в области нейронных сетей, математики и статистики.

Чем предстоит заниматься:

  • Выдвижение гипотез;
  • Работа с данными;
  • Работа с архитектурой модели, выдвижение идей по внесению изменений;
  • Анализ результатов экспериментов, проведенных на реальном автономном автомобиле.

Что мы ждем от кандидата:

  • Опыт обучения нейросетевых моделей и интеграции ML-модели в production-среду;
  • Уверенное знание и применение архитектуры Transformer, умение писать качественный код на Python, опыт работы с PyTorch;
  • Глубокие знания теоретических основ нейронных сетей, методов машинного обучения и понимание классических алгоритмов и структур данных;
  • Навык чтения статей об ML, контроль за развитием этой области и опыт работы с AI-агентами;
  • Умеете выдвигать гипотезы и выстраивать эксперименты для их проверки.

Будет плюсом:

  • Опыт работы с Cuda;
  • Опыт с TensorRT и оптимизацией производительности;
  • Знание С++ / ROS.
hh.ru
Зарплата не указанаРоссияМоскваГибрид

О команде:

Команда распознавания окружения решает задачи обучения нейронных сетей для распознавания в самых разных сценариях. Мы твёрдо знаем: создать эффективный нейросетевой алгоритм без качественных данных невозможно. Именно поэтому мы строим собственную инфраструктуру для подготовки, обработки и интеграции данных во фреймворки обучения моделей распознавания.

Мы в поисках ведущего разработчика-исследователя в области глубокого обучения и компьютерного зрения (Senior DL/CV Research Engineer) с глубоким знанием высшей математики и проективной геометрии. Специалисту предстоит создавать и оптимизировать нейросетевые алгоритмы восприятия для автономного транспорта, используя Python, базовый C++ и технологии параллельных вычислений (CUDA).

Чем предстоит заниматься:

  • Разработка интеллектуальных решений в области глубокого обучения/DL (компьютерное зрение/CV) задач в домене восприятия автономного транспорта;
  • Анализ современых подходов и реализация нейросетевых архитектур, обучение, проверка гипотез и проведение исследований;
  • Разработка и интеграция модулей нейронных сетей в готовый стек восприятия.

Что мы ждем от кандидата:

  • Уверенное знание глубокого обучения/DL (уровень погружения до самых глубоких вннутренних процессов обучения и research), машинного обучения/ML (понимание математики основных классических подходов), линейной алгебры (проективная геометрия, эпиполярная геометрия) и математического анализа, математической статистики;

  • Опыт работы с компьютерным зрением/Computer Vision задачам в разных доменах;

  • Умение писать продвинутый код на Python;

  • Умение писать и читать базовый код на C++;

  • Умение писать код для высоконагруженных систем (cuda, openml, mpi).

Сайты компаний
mayflower

Data Scientist (Search & Recommendations)

mayflowerАвтоподнятие
Зарплата не указанаКипрLimassolГибрид

Mayflower is a technology company building highload products used by millions of people worldwide. Operating at the scale of one of the world's top-50 websites, we solve complex engineering challenges and create solutions that power real-time entertainment for a global audience.

Now we look for a Data Scientist to join our ML team

Job Responsibilities

  • Search & Retrieval

    • Develop and improve retrieval pipelines for large-scale production search systems.

    • Work on candidate generation, query processing, matching, filtering, and retrieval strategies.

    • Improve search relevance, result coverage, and overall SERP quality.

    • Analyse failed searches, irrelevant results, zero-result queries, and other search-quality issues.

    • Explore lexical, semantic, behavioural, hybrid, and vector search approaches.

  • Ranking & Relevance

    • Build, train, and optimise ranking models for search and recommendation systems.

    • Develop learning-to-rank solutions using behavioural, content-based, contextual, and real-time features.

    • Design ranking features based on clicks, conversions, popularity, freshness, availability, and user behaviour.

    • Evaluate ranking quality using Precision, Recall, NDCG, MAP, MRR, and related relevance metrics.

    • Optimise models for low-latency inference and investigate relevance degradation, bias, and feedback loops.

  • Recommendation Systems

    • Develop recommendation models and candidate-generation strategies for personalised and non-personalised scenarios.

    • Build recall and ranking stages for multi-stage recommendation pipelines.

    • Work on related-item, complementary-item, next-action, and behavioural recommendation use cases.

    • Develop user, item, session, and contextual representations.

    • Balance relevance, diversity, novelty, coverage, and business constraints.

  • Experimentation & Evaluation

    • Design and run offline and online experiments for search, ranking, and recommendation improvements.

    • Build evaluation frameworks that connect model quality with product and business outcomes.

    • Design and analyse A/B tests using CTR, conversion, engagement, retention, and revenue-related metrics.

    • Create reproducible pipelines for data preparation, model training, evaluation, and comparison.

    • Evaluate model robustness across traffic segments, query groups, user cohorts, and edge cases.

  • ML Pipelines & Collaboration

    • Build end-to-end ML pipelines for feature generation, training, validation, deployment, and monitoring.

    • Work with high-load, real-time, and low-latency production systems.

    • Process large datasets using Python, SQL, batch pipelines, streaming systems, and Kafka.

    • Collaborate with product, backend, data engineering, and MLOps teams to productionise ML solutions.

    • Communicate technical decisions, experiment results, and trade-offs while contributing to ML best practices.

You’ll thrive here if you have

  • Strong hands-on experience building production search, ranking, or recommendation systems.

  • Strong Python and SQL skills for machine learning, data processing, and analytical queries.

  • Practical experience with learning-to-rank, candidate retrieval, search relevance, or recommender-system modelling.

  • Experience building and evaluating multi-stage retrieval and ranking pipelines.

  • Strong understanding of search and recommendation metrics, including Precision, Recall, NDCG, MAP, MRR, CTR, and conversion.

  • Experience with feature engineering and behavioural data such as impressions, clicks, sessions, and conversions.

  • Experience with ML libraries such as scikit-learn, XGBoost, LightGBM, CatBoost, PyTorch, or TensorFlow.

  • Experience working with large-scale production systems, distributed data processing, analytical databases, and streaming platforms.

  • Strong understanding of experimentation and A/B testing, with the ability to independently build, and validate ML solutions.

That can be a plus:

  • Experience with Elasticsearch, OpenSearch, Solr, Lucene, or another search-engine stack.

  • Experience with vector databases, approximate nearest neighbour search, and hybrid lexical-semantic retrieval.

  • Experience with query understanding, classification, spell correction, synonyms, or query expansion.

  • Experience with DSSM, two-tower models, BERT-based ranking, cross-encoders, or similar neural architectures.

  • Experience with large-scale data and ML platforms such as Airflow, MLflow

Conditions

We know that great talent deserves great conditions, so here's what you can expect when joining us:

  • EU-based employment contract and a 3-year Cyprus work visa with full support for your relocation and visa processes, including assistance for your family.

  • Full relocation package: flights to Limassol for you and your family, a company-covered apartment for the first month, and full relocation support to make your move smooth and hassle-free.

  • Transparent performance reviews twice a year, with bonus opportunities and salary adjustments.

  • Private medical insurance for you and your family, a corporate mobile plan (unlimited in Cyprus with roaming included), and interest-free support for car purchases.

  • Provident fund (Cypus): a long-term savings plan co-funded by you and the Company together (available after probation) that grows throughout your time with us in Cyprus.

  • Mindfulness & well-being support, including psychological assistance with 50% coverage.

  • 50% coverage of school and kindergarten fees for your children.

  • Fully covered sports benefits, and also access to in-house electric scooters and bike rentals, and cycling purchase compensation.

  • Investment in your growth: paid language courses and access to suited-for-you development programs, including conferences, training programs, and coaching to support your professional journey.

  • A culture of recognition: a peer reward program to celebrate your contributions.

  • A fully equipped office in Limassol’s city center, with everything you need for deep work and collaboration.

  • Free catering in the office and an in-house coffee bar with high-quality drinks and a health bar stocked with nutritious snacks.

  • A strong engineering culture: international teams, corporate events, team buildings, and hackathons—because great work happens in great communities.

Recruitment process

  • HR interview (40 min);

  • Technical interview (1.5 hour);

  • Final interview (45 min).

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