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Чётко описанная исследовательская стажировка в известном институте ИИ и робототехники предлагает работу над актуальными задачами, возможность публикаций и проверку методов на реальных роботах.
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Нужна аспирантураАктуальный стекПередовые исследованияПубликации и эксперименты
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Описание вакансии
Текст:
Hirify AI/Оригинал
TL;DR
Robotics Intern (Post-Training and Continual Learning) (reinforcement learning/robotics): Developing methods that adapt pretrained generalist robot policies to new tasks, environments, and robotic systems with an accent on offline-to-online reinforcement learning, imitation learning, simulation, and world models. Focus on designing rigorous simulation and real-robot experiments, analyzing results, and building data-efficient policy adaptation methods for reliable downstream performance.
Location: Los Altos, California, United States; hybrid, in-office role
Salary: $45–$65 per hour for California-based roles
Company
hirify.global develops tools and capabilities in AI, robotics, driving, and material sciences to improve the quality of human life.
What you will do
Develop post-training methods that adapt pretrained generalist robot policies to new tasks, environments, and robotic systems.
Design and conduct rigorous experiments in simulation and, where appropriate, on real robotic systems.
Analyze results, communicate findings, and contribute to research publications and internal technical reports.
Requirements
Currently pursuing a Ph.D. in Computer Science, Machine Learning, Robotics, or a related field.
Research experience in robot learning, reinforcement learning, imitation learning, generative modeling, world models, or a related area.
Interest in open research problems involving large-scale machine learning grounded in physical systems.
Proficiency in Python and a deep-learning framework such as PyTorch.
Ability to collaborate effectively with researchers and engineers and communicate research findings clearly.
Nice to have
Experience with pretrained generalist policies, foundation models, or large-scale robot-learning systems.
Familiarity with offline or online reinforcement learning, DAgger, interactive learning, or human-in-the-loop methods.
Experience with simulation, sim-to-real transfer, policy distillation, or robotic manipulation.
Experience with learned world models, model-based reinforcement learning, or planning.
Publication record or interest in publishing at leading venues such as CoRL, NeurIPS, ICLR, ICML, RSS, ICRA, IROS, or related conferences and journals.
Interest in translating fundamental research into reliable methods evaluated on real robotic systems and practical downstream tasks.
Culture & Benefits
Paid 12-week internship.
Hybrid, in-office work arrangement.
Medical, dental, and vision insurance.
Paid time off, including holiday pay and sick time.
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