Robotics Simulation Systems Engineer

Field AI is transforming how robots interact with the real world. We are building risk‑aware, reliable, and field‑ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data-driven approaches or pure transformer‑based architectures, and are charting a new course, with already globally deployed solutions delivering real‑world results and rapidly improving models through real‑field applications.

You’ll build and own the simulation stack that powers development and testing for legged robots, humanoids, and car‑like platforms. The work spans software‑in‑the‑loop (SIL) and hardware‑in‑the‑loop (HIL) setups, GPU‑accelerated physics and learning loops, synthetic data generation, and rigorous Monte Carlo evaluation— with clear, visual reporting. You’ll also scale sim farms and data pipelines in AWS and work hands‑on in Gazebo/Ignition and NVIDIA Isaac Sim
.

• 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 Py Torch .
• 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/ROS2 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).

• 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 ROS2 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 Py Torch 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).

• Deep expertise with simulator internals and advanced features (Isaac Sim/Omniverse USD, Gazebo/Ignition plugins), plus Mu Jo Co  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/Cloud Formation).
• Safety‑critical mindset and familiarity with…