Embodied AI Lead / Robot Brain Lead

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.

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.

Your Role:

As Embodied AI Lead or Robot Brain Lead, you will own the overall Embodied AI architecture for our platform:

• System 2 – vision-language(-action) models for task understanding, planning, and dialogue.
• System 1 – visuomotor policies for whole-body control and manipulation.
• World models – for imagination, planning, and predictive safety.

You are both an architect and a hands-on technical leader, guiding a small team to turn cutting-edge research into robust, production-ready capabilities.

Key Responsibilities:

Architecture & roadmap

• Design the overall Robot Brain architecture:
• System 2: VLM/LLM/VLA for semantic understanding and task planning.
• System 1: visuomotor control for locomotion and manipulation.
• World model integration for rollouts and safety.
• Define a short-term and long-term technical roadmap balancing innovation and deliverables.

Model strategy & implementation

• Make key decisions on model families and training strategies:
• E2E visuomotor, diffusion policies, transformer policies, VLA-based approaches, RL/BC hybrids.
• Lead the implementation and deployment of the first production version of the Robot Brain on at least one robot platform.
• Drive iterative improvements as we expand tasks and sites.

Cross-team integration

• Collaborate with Control & Robotics:
• Co-design skills (navigate, pick, place, open, inspect, etc.).
• Ensure Brain outputs are physically feasible and robust.
• Collaborate with Agent & Applications:
• Align Brain capabilities with real workflows and job definitions.
• Collaborate with Platform & Data:
• Define training data schemas and logging requirements.
• Build end-to-end training and deployment pipelines.

Safety, evaluation & reliability

• Define evaluation protocols for the Brain:
• Task success, robustness, long-horizon stability, safety incident metrics.
• Work with World Model & Safety teams to incorporate predictive safety and scenario-based testing.
• Ensure the Brain is observable, debuggable, and improvable in production.

Team leadership

• Hire, mentor, and lead a small Embodied AI team (3–6 engineers/researchers initially).
• Establish high standards for engineering quality, experimentation, and documentation.
• Represent the Embodied AI function in technical and cross-functional planning (product, hardware, operations).

Basic Qualifications:

• Bachelor’s or higher in CS, Robotics, EE, or related field.
• 5+ years in ML / robotics, including 2+ years in a lead / tech lead role.
• Deep experience in at least two of:
• Proven track record deploying ML models on real robotic hardware, not just simulation.
• Strong coding skills in Python and experience with PyTorch or JAX, multi-GPU training.
• Strong written and verbal communication; able to write clear design docs and align stakeholders.

Preferred Qualifications:

• PhD or equivalent industry research experience in Robotics, AI, Machine Learning, or a related field.
• Hands-on experience with foundation models for robotics, such as VLMs, VLAs, or multimodal LLM-based agents applied to real-world embodied tasks.
• Experience building or scaling robot learning systems in production, including data collection at scale, sim-to-real transfer, and continuous learning loops.

Annual Salary Range:

($180,000.00 - $250,000.00 DOE), plus benefits and incentive plans