Robotics Simulation Systems Engineer

Position: 3.22 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.

About the Job

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
.

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 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).

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 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).

The Extras That Set You Apart

• 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 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.

Compensation and Benefits

Our salary range is between ($70,000 - $300,000 annual), but we take into consideration an individual's background and experience in determining final salary; base pay offered may vary considerably depending on geographic location, job-related knowledge, skills, and experience. Also, while we enjoy being together on-site, we are open to exploring a hybrid or remote option.

Why Join Field AI?

We are solving one of the world's most complex challenges: deploying robots in unstructured, previously unknown environments. Our Field Foundational Models™ set a new standard in…