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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.
Founded in 2014, the Company’s mission is to disrupt the automotive and technology industries by creating user-centric, technology-first experiences. The Company, together with its controlled subsidiaries, operates across multiple technology-driven areas, including AI electric vehicles, robotics, and its crypto business (AIXC), all under its upgraded Global EAI Industry Bridge Strategy, marking the beginning of a new chapter in AI mobility and Web3 integration. The Company aims to leverage the latest technologies and world’s best talent to realize exciting new possibilities across all of these lines. Faraday Future’s automotive business exemplifies its vision for luxury, innovation, and performance, while its FX strategy aims to introduce mass production models equipped with state-of-the-art luxury technology derived from the FF brand, targeted towards a broader market with middle-to-low price range offerings. FF is committed to redefining mobility through AI innovation. Join us in shaping the future of intelligent transportation and technology by creating something new, something connected, and something with a true global impact.
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Design and execute post-training pipelines for VLA and visuomotor policy models (e.g., diffusion policies, ACT, flow matching), including supervised fine-tuning (SFT), reinforcement learning (RL), and preference-based optimization
Fine-tune pretrained robot foundation models on task-specific demonstration datasets for dexterous manipulation, locomotion, whole-body control, and multi-step task sequencing
Develop and iterate on reward functions, verifiers, and RL training loops (PPO, GRPO, RLVR) to improve policy success rate and robustness in simulation and real-world deployment
Apply parameter-efficient fine-tuning methods (LoRA, QLoRA, OFT) to adapt large models to new tasks and robot embodiments under compute constraints
Build and manage large-scale robot demonstration data pipelines: teleoperation data collection, action tokenization (e.g., FAST tokenizer), data augmentation, quality filtering, and dataset versioning
Define data collection strategies across robot platforms, collaborating with robot operators and data labeling teams to ensure dataset diversity and coverage
Integrate multi-modal sensory data (RGB, depth, proprioception, force/torque, tactile) into coherent training datasets
Build and maintain simulation environments (Isaac Sim, MuJoCo, SAPIEN) for scalable policy training, including domain randomization, asset generation, and task definition
Address sim-to-real transfer challenges through visual augmentation, action space calibration, dynamics randomization, and systematic real-world validation
Design and run large-scale distributed RL training across GPU clusters for locomotion and manipulation policies
Build evaluation and benchmarking infrastructure: automated success-rate tracking, sim evaluation harnesses, real-robot A/B testing, and regression monitoring
Optimize models for on-robot inference: quantization (INT8/FP8), action chunking, latency reduction, and real-time control loop integration
Collaborate with controls, perception, and hardware teams to integrate learned policies into the full robot software stack
Track and adopt state-of-the-art research in robot foundation models, generalist policies, and embodied AI post-training (e.g., π₀/π₀.5, OpenVLA OFT, RT-2, Octo, Helix)
Contribute to internal research efforts on topics such as multi-embodiment transfer, long-horizon task learning, open-world generalization, and human-in-the-loop policy improvement
Public U.S. electric-vehicle and embodied-AI robotics company developing intelligent mobility and robotics products.
Visit company websiteJobs and hiring trendsUSD 150000-180000 yearly / year
Full-time
Senior · 3+ years experience
Onsite
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