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.

QUALIFICATIONS

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• 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

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• 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).

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• 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.