Senior Simulation Engineer

Responsibilities

Build a visually realistic simulator to test full end-to-end autonomy stack behavior, from simulating sensors to motion planning, across a range of scenario conditions.

Prototype and integrate with internal and third-party simulators to evaluate their ability to support learned system testing.

Curate scenarios, system introspection.

Build data logging frameworks used during large-scale virtual tests.

Collaborate closely with autonomy, ML, and integration teams to define simulation entry points, runtime configs, and closed-loop evaluation metrics.

Build diagnostic tooling and analysis pipelines to understand and improve real system behavior in simulation.

Lead cross-functional efforts to close the gap between simulation and on-vehicle deployment, increasing the reliability of sim-based validation.

Provide technical mentorship and foster a collaborative, high-trust engineering culture across organizational boundaries.

Demonstrate excellent design practices; generate technical documentation; lead technical presentations; aligning with stakeholders before, during, and after implementation is essential.

Qualifications

Bachelor’s or Master’s in Computer Science, Robotics, or a related field.

10+ years of experience in robotics, autonomous systems, or simulation.

Experience with 3D reconstruction (e.g. Gaussian Splatting, Neural radiance fields, etc).

Experience with 3D generation.

Experience with Unreal Engine.

Strong programming skills in Python and C++, especially for robotics or systems development.

Experience with simulation platforms (e.g., CARLA, Applied Intuition, Nvidia DriveSim, etc) and their integration into autonomous system workflows.

Knowledge of sensor simulation principles and how perception systems interact with synthetic data.

Understanding of end-to-end autonomy pipelines, from raw sensor input to trajectory outputs.

Demonstrated ability to design for both users (e.g., autonomy developers) and simulation infrastructure stakeholders.

Passion for using simulation to drive real-world progress and system understanding.

Bonus Qualifications

Hands-on experience validating machine learning-based autonomy stacks in closed-loop simulation.

Knowledge of scenario generation, rare event simulation, or counterfactual testing.

Knowledge of one or more cloud compute platforms, such as AWS.

Experience with multi-agent simulation or high-fidelity 3D environments.

Prior experience in fast-paced R&D environments bridging research and production.