Robotics AI Research Engineer

About the role

Join our Humanoid Robotics R&D team to develop the intelligence behind next-generation humanoid robots. You will research, train, fine-tune, evaluate, and deploy Vision-Language Models (VLMs) and Vision-Language-Action (VLA) models, connecting foundation-model research with real robotic hardware.

Key responsibilities

• Fine-tune and post-train VLM / VLA foundation models using internal humanoid robotics datasets.
• Develop SFT, preference-optimization, reinforcement-learning, and imitation-learning pipelines using simulation and real-hardware feedback.
• Research hierarchical robotic policies combining high-level reasoning, skill planning, and low-level whole-body control.
• Run large-scale distributed training in multi-GPU environments using frameworks such as FSDP / DeepSpeed.
• Design robotics data strategies covering teleoperation, motion capture, human video, synthetic simulation, and external datasets.
• Build reproducible evaluation pipelines in simulation and on real humanoid hardware.
• Drive sim-to-real transfer through domain randomization, system identification, distillation, and policy learning.
• Deliver production-ready checkpoints and inference pipelines for edge and GPU environments.
• Collaborate with Data Factory and Platform Engineering teams to convert research into deployable robot capabilities.
• Mentor engineers and contribute to technical research, patents, or publications where appropriate.

About you

• MSc or PhD in Computer Science, Robotics, Machine Learning, Electrical Engineering, or related discipline, or equivalent industrial research experience.
• 3+ years of hands-on experience training or post-training large neural networks using PyTorch.
• Experience with at least one of: Vision-Language Models, Vision-Language-Action models, diffusion / flow-matching policies, or large-scale imitation learning.
• Experience with modern LLM/VLM frameworks such as Hugging Face Transformers, TRL, LLaMA-Factory, Axolotl, Unsloth, or equivalent.
• Experience with distributed training frameworks such as FSDP, DeepSpeed, or Accelerate.
• Strong foundations in robotics, including kinematics/dynamics, control, imitation learning, and reinforcement learning.
• Experience with robotics simulation such as Isaac Sim, MuJoCo, PyBullet, or Genesis.
• Strong Python engineering and modern software/MLOps practices.
• Proven ability to move trained models beyond research experiments into real systems.
• Strong English communication skills.