Physical AI Engineer
About The Position
We’re building real‑world Physical AI systems—where learning agents interact with physical machines. This role is tailored for an AI Robotics Engineer with a strong reinforcement learning (RL) mindset, someone who wants to train, evaluate, and deploy intelligent behaviors that emerge through interaction, not just perception. You’ll work hands‑on with NVIDIA Omniverse, physics‑based simulation, synthetic data, and foundation models to train agents that learn to act, adapt, and generalize in complex environments. This is a builder role: fast iteration, scalable training, and direct transfer from simulation to physical robots.
Requirements
- 5–8+ years of experience in robotics, reinforcement learning, simulation, or applied machine learning.
- Strong hands‑on experience with reinforcement learning, imitation learning, or learning‑based control.
- Proven experience building and training agents in simulation environments (synthetic data, domain randomization, curriculum learning).
- Experience working in the NVIDIA ecosystem (Omniverse, Isaac Sim, USD pipelines strongly preferred).
- Experience applying or integrating foundation models (e.g., GPT‑based or Claude / Opus‑style models) into robotics or decision‑making workflows.
- Strong Python skills; ability to build and scale training pipelines.
- Builder mindset with a track record of moving learning systems from experiment to deployment.
Nice To Haves
- Model‑free or model‑based RL at scale
- Sim‑to‑real transfer techniques (domain randomization, system ID, hybrid control)
- Physics engines, real‑time systems, or robotics middleware
- Experience operationalizing learned policies on physical robots
Responsibilities
- Build high‑fidelity, physics‑based simulation environments in NVIDIA Omniverse / Isaac Sim for training and evaluating robotic agents.
- Design and run reinforcement learning and imitation learning pipelines using simulation‑generated and synthetic data.
- Train and tune policies for control, planning, and decision‑making, emphasizing robustness and sim‑to‑real performance.
- Integrate foundation models (GPT‑class, Claude / Opus, multimodal models) to support reasoning, task decomposition, or human‑in‑the‑loop learning.
- Drive simulation‑to‑real transfer, ensuring learned behaviors perform reliably on physical robotic systems.
- Collaborate with robotics, controls, and perception engineers to deploy learning‑based systems end to end.
Benefits
- Elective Benefits: Our programs are tailored to your country to best accommodate your lifestyle.
- Grow Your Career: Accelerate your path to success (and keep up with the future) with formal programs on leadership and professional development, and many more on-demand courses.
- Elevate Your Personal Well-Being: Boost your financial, physical, and mental well-being through seminars, events, and our global Life Empowerment Assistance Program.
- Diversity, Equity & Inclusion: It’s not just a phrase to us; valuing every voice is how we succeed. Join us in celebrating our global diversity through inclusive education, meaningful peer-to-peer conversations, and equitable growth and development opportunities.
- Make the Most of our Global Organization: Network with other new co-workers within your first 30 days through our onboarding program.
- Connect with Your Community: Participate in internal, peer-led inclusive communities and activities, including business resource groups, local volunteering events, and more environmental and social initiatives.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
