Senior Simulation Engineer

At Toyota Research Institute (TRI), we’re on a mission to improve the quality of human life. We’re developing new tools and capabilities to amplify the human experience. To lead this transformative shift in mobility, we’ve built a world-class team advancing the state of the art in AI, robotics, driving, and material sciences.

The Automated Driving Advanced Development division at TRI will focus on enabling innovation and transformation at Toyota by building a bridge between TRI research and Toyota products, services, and needs. We achieve this through partnership, collaboration, and shared commitment. This new division is leading a new cross-organizational project between TRI and Woven by Toyota to conduct research and develop a fully end-to-end learned driving stack. This cross-org collaborative project is harmonious with TRI’s robotics divisions' efforts in Diffusion Policy and Large Behavior Models.

We are seeking a Senior Simulation Engineer to lead the development of sensor and system-level simulation workflows that support both closed-loop validation and synthetic data generation for training. In this role, you'll help build the simulated environments, data pipelines, and interfaces required to evaluate and improve our full-stack driving policy under diverse, realistic conditions.

This role is not limited to simulation infrastructure or tooling. Instead, you will focus on functional validation of learned behaviors, scalable synthetic data generation, and the seamless integration of state-of-the-art simulation technologies to support both training and evaluation workflows. You will also play a key role in driving cross-functional alignment between autonomy, platform, ML infrastructure, and integration teams. This work is part of Toyota’s global AI efforts and will be conducted in close collaboration with teams across TRI, Woven by Toyota, and other engineering partners.

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.

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.

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.

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