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Selected role

Machine Learning Engineer

Toyota Research Institute Los Altos, CA

$264k Senior Full-time

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 in Automated Driving, Energy & Materials, Human-Centered AI, Human Interactive Driving, Large Behavioral Models, and Robotics.

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 looking for a Machine Learning Engineer to join our autonomy team and help bring end-to-end ML models ( from pixels to trajectories ) into robust, testable, and deployable systems. This role is ideal for engineers who thrive at the intersection of machine learning, systems engineering, and real-world deployment.

You’ll contribute to the implementation, evaluation, and integration of ML-based components for perception, planning, and control. This includes supporting our team’s efforts in simulation-based testing, real-time deployment, and data-driven model development. You’ll work closely with researchers, data engineers, and autonomy engineers to ensure models scale from prototype to production. This work is part of Toyota’s global AI efforts to build a more coordinated global approach across Toyota entities.

Responsibilities

Implement, maintain, and evaluate end-to-end ML models used in the autonomy stack
Collaborate with ML researchers, data scientists and engineers, and simulation teams to build training, evaluation, and deployment pipelines.
Integrate models into real-time systems running on simulation and vehicle platforms, ensuring correctness and performance.
Support open-loop, closed-loop, and batch evaluation workflows of trained models, including metrics tracking, ablation studies, and debugging tools.
Help design scalable workflows for managing large datasets (e.g., demonstration driving for imitation learning) and support diverse scenario coverage.
Write clean, modular, well-tested code with a focus on reliability, clarity, and maintainability.

Qualifications

Bachelor’s or Master’s degree in Computer Science, Robotics, Engineering, or a related field.
3+ years of strong experience with ML frameworks such as PyTorch, Tensorflow or Caffe.
Strong Python and C++ programming skills and solid understanding of ML model development best practices.
Familiarity with metrics dashboards, experiment tracking, and ML ops tooling (e.g., Weights & Biases, MLflow, Metaflow).
Some experience working with robotics or real-world sensor data (e.g., video, lidar, IMU, or radar).
Strong understanding of version control, testing, and software engineering fundamentals.
Enthusiasm for collaborative engineering and building reliable ML systems that support real-world autonomy.

Bonus Qualifications

Experience working with ROS, simulation frameworks (e.g., CARLA, Nvidia DriveSim), or vehicle interfaces.
Experience with model deployment with NVIDIA stack (e.g. ONNX graphs, TensorRT, profiling)
Exposure to distributed training, inference optimization, or model deployment on edge devices.
Familiarity with recent breakthroughs in ML (e.g. foundation models, pre-training and efficient fine-tuning, multimodal Transformer architectures).

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 $176,000 and $264,000/year for California-based roles. Base pay offered may vary depending on multiple individualized factors, including, but not limited to, business or organizational needs, market location, job-related knowledge, skills, and experience. Note that TRI offers a generous benefits package (including 401(k) eligibility and various paid time off benefits, such as vacation, sick time, and parental leave) and an annual cash bonus structure. Details of participation in 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.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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