Staff Engineer, Physical AI & Simulation

Veeda AI; California; On-site; USD 120,000 - 180,000; Full time

Job summary

Veeda AI is pioneering multimodal world models for Physical AI in California. You will help integrate world models with simulator APIs such as Isaac Lab and Carla, and build asset pipelines for OpenUSD and related formats.

The role emphasizes enabling deterministic, reproducible training cycles and bridging gaps between simulators and hardware, collaborating with engineers and researchers across AI robotics labs.

Qualifications

• Bachelor's degree or equivalent hands-on experience in CS/CG/Robotics
• Experience building or substantially extending a physics engine or simulation framework
• Strong C++ and Python skills; GPU coding with CUDA/NVIDIA Warp
• Fluency in 3D content pipelines (OpenUSD, glTF, meshes) and their intricacies
• Ability to measure simulation fidelity and close gaps between simulators and hardware

Responsibilities

• Simulator integration: connect world models to simulator APIs (Isaac Lab, Carla)
• Harness engineering: adapt classical code to physical AI harness
• Synthetic data generation: collect success/failure cases for both algorithms
• Scene & Asset Pipelines: build procedural scene generation on OpenUSD, URDF, MJCF with QA
• Throughput & Determinism: ensure replayable rollouts with fixed seed/config
• Environment API & Fidelity: own interface policy training runs against Gymnasium-style APIs and regression benchmarks

Skills

C++

Python

GPU programming (CUDA/NVIDIA Warp)

3D content pipelines

Physics engine / simulation framework

Education

Bachelor's degree or equivalent hands-on experience in Computer Science, Computer Graphics, Robotics, or related field

Tools

OpenUSD tooling

Omniverse Kit extensions

MuJoCo

NVIDIA Warp

Job description

Veeda AI is pioneering multimodal world models for Physical AI in California. You will help integrate world models with simulator APIs such as Isaac Lab and Carla, and build asset pipelines for OpenUSD and related formats.

The role emphasizes enabling deterministic, reproducible training cycles and bridging gaps between simulators and hardware, collaborating with engineers and researchers across AI robotics labs.