10 дней назад
Staff Research Scientist, Reinforce Learning (Embodied AI)
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Staff Research Scientist, Reinforce Learning (Embodied AI): Building world models, planners, reinforcement learning systems, and geometric foundation models for autonomous driving with an accent on simulation, spatial intelligence, multimodal learning, and real-world deployment. Focus on designing scalable decision-making architectures, advancing sim-to-real transfer, and defining benchmarks for long-horizon prediction and driving performance.
Location: London, United Kingdom; full-time role based in the London office with a hybrid working policy
Company
develops embodied AI software and foundation models for autonomous driving.
What you will do
- Develop world models and planners using diffusion-based, autoregressive, or hybrid approaches for realistic simulation.
- Advance reinforcement learning and reward modeling across real and synthetic data.
- Develop geometric foundation models for 3D spatial understanding in dynamic environments.
- Enable cross-embodiment robotic learning with multimodal foundation models.
- Research scaling laws, generalisation, and sim-to-real transfer.
- Define evaluation frameworks and benchmarks for long-horizon prediction, scene fidelity, and driving performance.
Requirements
- 3+ years of experience developing and deploying ML systems in real-world or production settings.
- PhD, Master’s degree, or equivalent experience in machine learning, computer vision, robotics, or a related field.
- Deep expertise in embodied AI, including foundation models, generative world modeling, reinforcement learning, reward modeling, or spatial AI.
- Track record of publications at top-tier machine learning, computer vision, or robotics conferences.
- Strong Python programming skills and experience with PyTorch.
- Experience with large-scale datasets and evaluation, plus strong problem-solving and interdisciplinary collaboration skills.
Nice to have
- Experience in autonomous driving, robotics, or simulation systems.
- Familiarity with large-scale training tools such as FSDP, DeepSpeed, or JAX.
- Experience with sim-to-real transfer or data-efficient learning.
- Contributions to open-source ML tools or research infrastructure.
Culture & Benefits
- Relocation support with visa sponsorship.
- Flexible working hours within the hybrid working model.
- Salary and equity compensation.
- Learning and development opportunities.
- Private health insurance, enhanced parental leave, workplace nursery scheme, therapy, daily yoga, onsite chef, and social budgets.
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