Physical AI Engineer - Simulation & Synthetic Data

About the role

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.

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 (e.g., 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.

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-class, 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.
• Must have hands-on experience training robotic agents or systems (in simulation and/or real-world environments).

Nice to Have

• 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.

Benefits

At TD SYNNEX, we value diversity, equity, and inclusion and are committed to building a team that represents and empowers a variety of backgrounds, perspectives, and skills. We offer competitive compensation, comprehensive benefits, and a supportive work environment.

Pay

Competitive salary based on experience and qualifications.

Schedule

Remote/hybrid (US-based), occasional domestic and global travel, flexible working hours aligned to experimentation and training cycles.

What's In It For You?

• Elective Benefits: Programs tailored to your country to best accommodate your lifestyle.
• Growth and Development: Formal programs on leadership and professional development, and many more on-demand courses.
• Personal Well-Being: Seminars, events, and our global Life Empowerment Assistance Program.
• Diversity, Equity & Inclusion: Celebrating our global diversity through inclusive education, meaningful peer-to-peer conversations, and equitable growth and development opportunities.
• Global Organization: Network with other new colleagues within your first 30 days through our onboarding program.
• Community Engagement: Participate in internal, peer-led inclusive communities and activities, including business resource groups, local volunteering events, and more environmental and social initiatives.