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. Please include links to any relevant open-source contributions or technical project write-ups with your application. The pay range for this position at commencement of employment is expected to be between $180,000 and $258,750/year for California-based roles. Base pay offered will depend on multiple individualized factors, including, but not limited to, a candidate's experience, skills, job-related knowledge, and market location. TRI offers a generous benefits package including medical, dental, and vision insurance, 401(k) eligibility, paid time off benefits (including vacation, sick time, and parental leave), and an annual cash bonus structure. Additional details regarding these benefit plans will be provided if an employee receives an offer of employment. Please reference this Candidate Privacy Notice to inform you of the categories of personal information that we collect from individuals who inquire about and/or apply to work for Toyota Research Institute, Inc. or its subsidiaries, including Toyota A.I. Ventures GP, L.P., and the purposes for which we use such personal information. TRI is fueled by a diverse and inclusive community of people with unique backgrounds, education and life experiences. We are dedicated to fostering an innovative and collaborative environment by living the values that are an essential part of our culture. We believe diversity makes us stronger and are proud to provide Equal Employment Opportunity for all, without regard to an applicant’s race, color, creed, gender, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, medical condition, religion, marital status, genetic information, veteran status, or any other status protected under federal, state or local laws. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. Pursuant to the San Francisco Fair Chance Ordinance, we will consider qualified applicants with arrest and conviction records for employment. 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.

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