Tech Lead, Robotic AI Model

The role

You will own the pipeline that transforms pretrained foundation models into deployable robot policies. This involves fine-tuning VLA models, training reinforcement learning policies in simulation, and validating behaviors on real hardware.

What you will do

• Design and execute post-training pipelines for VLA and visuomotor policy models
• Fine-tune pretrained robot foundation models on task-specific demonstration datasets
• Develop and iterate on reward functions, verifiers, and RL training loops
• Apply parameter-efficient fine-tuning methods to adapt large models
• Build and manage large-scale robot demonstration data pipelines
• Define data collection strategies across robot platforms
• Integrate multi-modal sensory data into coherent training datasets
• Build and maintain simulation environments for scalable policy training
• Address sim-to-real transfer challenges
• Design and run large-scale distributed RL training across GPU clusters

Required qualifications

• Master’s or PhD in Robotics, Computer Science, Machine Learning, or a closely related field
• 3+ years of hands-on experience in robot learning, including imitation learning, behavior cloning, or visuomotor policy training on real or simulated robots
• Deep expertise in at least one post-training paradigm: SFT on robot demonstrations, RL-based policy optimization, or diffusion/flow-matching policy training
• Strong PyTorch skills with experience training and debugging models at scale
• Familiarity with distributed training (FSDP, DeepSpeed)
• Practical experience with robot simulation platforms (Isaac Sim, MuJoCo, PyBullet, or SAPIEN) and sim-to-real workflows
• Understanding of action representations for robotics: continuous control, discrete tokenization, action chunking, and diffusion-based action generation
• Solid Python engineering
• Comfortable working with ROS/ROS2, real-time control systems, and robot hardware integration
• Ability to independently drive projects from research prototype to real-robot deployment

Nice to have

• Experience fine-tuning VLA models such as π₀, OpenVLA, RT-2, Octo, or similar generalist robot policies
• Hands-on experience with real robot platforms: humanoids, bi-manual arms (ALOHA), mobile manipulators, or dexterous hands
• Experience with large-scale teleoperation data collection systems and robot fleet management
• Familiarity with RLHF/DPO/GRPO applied to robotic policy alignment and human preference learning
• Experience building or contributing to robot learning infrastructure (LeRobot, robomimic, openpi, etc.)
• Publications at top robotics or ML venues (CoRL, RSS, ICRA, NeurIPS, ICML, ICLR)
• Knowledge of on-device model optimization: TensorRT, ONNX Runtime, model pruning, and edge deployment for embodied AI

Benefits

• Healthcare + dental + vision benefits (Free for you/discounted for family)
• 401(k) options
• Casual dress code + relaxed work environment
• Culturally diverse, progressive atmosphere

About the company

Faraday Future is a California-based technology company focused on the design, engineering, and development of intelligent, connected electric vehicles and related artificial intelligence–enabled technologies. The Company operates across multiple technology-driven areas, including AI electric vehicles, robotics, and its crypto business (AIXC).