Senior Software 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 looking for a Senior Software Engineer to join our end-to-end automated driving team, supporting the integration, prototyping, and deployment of advanced autonomy systems on vehicle platforms. As a software generalist with deep systems knowledge, you will work across the autonomy software stack to accelerate feature development, streamline system-level integration, and help validate both closed-course and public road deployments.

The ideal candidate has strong modern C++ (C++14/17/20) and Python programming experience, a robust understanding of robotics or embedded software systems, and thrives in collaborative, high-velocity engineering environments. This role bridges research and real-world deployment, focusing on engineering support for platform integration, evaluation tooling, system bring-up, and diagnostics. You'll partner closely with end-to-end machine learning, simulation and infrastructure teams to ensure that the full stack runs robustly on real vehicles in closed-course, public road and simulation testing.

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.

Design, implement, and maintain robust software in C++ and Python, that supports ML training, evaluation, and inference workflows.

Build and maintain ML tooling for dataset handling, experiment tracking, metrics computation, and offline/online analysis.

Enable model export and edge inference prototyping, including model packaging, runtime integration, and performance validation on embedded compute platforms.

Build diagnostics, monitoring, logging, and introspection tools that provide visibility into runtime end-to-end machine learning model behavior and help accelerate iteration.

Collaborate with ML researchers to translate experimental models into repeatable, production-ready pipelines.

Support CI and automation for training, evaluation, and inference workflows.

Partner with cross-functional teams to support software deployment and versioning, ensuring consistent behavior across environments.

Apply rigorous engineering best practices, including code review, documentation, and testing, to deliver robust and maintainable systems.

Bachelor or master degree in Computer Science, Robotics, or a related field.

10+ years of relevant software development experience, ideally in robotics, automotive, embedded systems, or distributed platforms.

Strong proficiency in modern C++ (C++14/17/20) and Python.

Familiarity with Linux systems programming (e.g., sockets, filesystems, threading) and real-time systems.

Experience building ML platforms, data pipelines, or distributed software systems and supporting machine learning training or inference pipelines.

Familiarity with ML frameworks (PyTorch, TensorFlow), model deployment tools (TensorRT, ONNX, TorchScript) and inference runtimes.

Familiarity with Linux-based development environments and production debugging.

Experience integrating and debugging complex software systems, ideally in robotic or automated driving platforms.

Proven ability to work hands-on and cross-functionally to solve real-world deployment issues.

Experience in automated driving, robotics, or simulation-based system testing.

Hands-on experience with embedded systems development, including work on platforms such as NVIDIA Jetson Orin, Qualcomm Snapdragon Ride, or similar automotive-grade SoCs.

Familiarity with container orchestration (Docker, Kubernetes), or orchestration tools for testing and deployment.

Experience working with distributed compute systems, large-scale data logging, or introspection frameworks.

Understanding of automotive software practices and standards (e.g., ISO 26262, safety-critical development).

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

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