3.22 Robotics Simulation Systems Engineer

What You’ll Get To Do

• Own SIL/HIL simulation infrastructure
• Design, implement, and maintain SIL/HIL rigs, including real‑time loops, I/O, and fault‑injection.
• Integrate simulators into CI/CD for repeatable, automated regression testing.
• Develop sensor/actuator interfaces and bring‑up procedures for lab and field use.
• Model robot and vehicle dynamics
• Build and validate dynamics models for legged systems, humanoids, and car‑like platforms.
• Implement contact/friction models, parameter identification, and sensor/terrain effects.
• Create configurable scenarios, environments, and disturbances for coverage testing.
• Accelerate workloads on GPU
• Parallelize simulation, perception, and policy evaluation using CUDA and PyTorch.
• Profile and optimize kernels, memory movement, and mixed CPU/GPU pipelines.
• Scale Monte Carlo campaigns across local GPUs and AWS (e.g., EC2/EKS/Batch) for fast iteration.
• Generate data and close the ML loop
• Produce synthetic datasets with domain randomization and high‑fidelity annotations.
• Connect simulation output to training pipelines; track dataset versions and metrics.
• Manage datasets in AWS S3 and wire up distributed processing for large data volumes.
• Plan experiments and report results
• Design statistically sound Monte Carlo studies and acceptance tests.
• Visualize performance with plotting libraries (e.g., Bokeh/Matplotlib); publish dashboards.
• Summarize findings in clear engineering reports and reviews.
• Integrate with robotics software
• Build ROS/ROS 2 nodes, messages, and tooling; simulate sensors and networks.
• Develop and test in Gazebo/Ignition and NVIDIA Isaac Sim (plugins, sensor models, physics configs).
• Create reproducible dev environments (Docker, CMake) and enforce code health (linting, tests).

What You Have

• 3+ years building robotics simulation or controls software (or equivalent research/industry experience).
• Strong coding in Python and C/C++, plus solid Linux, Git, CI/CD, and containerization practices.
• Hands‑on experience building SIL/HIL setups, including real‑time constraints and hardware I/O.
• Proficiency with ROS and ROS 2 development (nodes, topics, services, bags).
• Hands‑on with Gazebo and NVIDIA Isaac Sim (sensor plugins, physics configuration).
• Solid background in rigid‑body kinematics/dynamics, contact/friction, and basic state estimation.
• Proven GPU experience: CUDA programming and PyTorch for accelerated simulation/ML loops.
• AWS experience for simulation and data pipelines (e.g., EC2 GPU instances, EKS/Batch, S3, IAM).
• Ability to design and run Monte Carlo simulations and report results with Bokeh/Matplotlib.
• Synthetic data generation for ML training (domain randomization, labeling, dataset versioning).

The Extras That Set You Apart

• Deep expertise with simulator internals and advanced features (Isaac Sim/Omniverse USD, Gazebo/Ignition plugins), plus MuJoCo or Chrono.
• Prior work on legged/humanoid control or car‑like dynamics (trajectory planning, MPC, tire/ground models).
• Sensor simulation depth (cameras, LiDAR, IMU) with realistic noise and distortion models.
• Distributed compute for large‑scale simulation (multi‑GPU, Ray/Kubernetes) and AWS infra‑as‑code (Terraform/CloudFormation).
• Safety‑critical mindset and familiarity with robotics/automotive standards
• Publications, patents, or significant open‑source contributions in simulation, control, or robotics tooling.
• Experience bringing up real robots and closing the sim‑to‑real gap through calibration and validation.