Machine Learning Engineer, Behavior (Internship)

Woven by Toyota is enabling Toyota’s once-in-a-century transformation into a mobility company. Inspired by a legacy of innovating for the benefit of others, our mission is to challenge the current state of mobility through human-centric innovation — expanding what “mobility” means and how it serves society. Our work centers on four pillars: AD/ADAS, our autonomous driving and advanced driver assist technologies; Arene, our software development platform for software-defined vehicles; Woven City, a test course for mobility; and Cloud & AI, the digital infrastructure powering our collaborative foundation. Business-critical functions empower these teams to execute, and together, we’re working toward one bold goal: a world with zero accidents and enhanced well-being for all. TEAM At Woven by Toyota, we tackle Autonomy challenges at the intersection of AI, Robotics, and Advanced Driving. Our work includes a diverse array of challenges and activities, such as analyzing petabytes of multimodal driving data, solving optimization problems in computer vision, minimizing latency on hardware accelerators, deploying scalable and efficient machine learning (ML) training and evaluation pipelines, and designing novel neural network architectures to advance state-of-the-art ML for Perception, Prediction, and Motion Planning. We are looking for doers and creative problem solvers to join us in improving mobility for everyone with human-centered automated driving solutions for personal and commercial applications. The Behavior Actuation team develops state-of-the-art vehicle control and estimation algorithms from prototype to production. We leverage control theory, optimization, and machine learning to design and implement real-time vehicle control solutions with measurable impact on autonomy performance.  WHAT WE'LL GIVE YOU This internship role is an opportunity to prototype and test estimation algorithms for robust vehicle control in challenging driving conditions such as inclement weather. In this role, you will collaborate with technical experts in control, estimation, and machine learning to design algorithms to boost closed-loop controller performance and prove the strategy in simulation or on a prototype vehicle.  Woven by Toyota internships are structured to ensure you grow both technically and professionally. You will get a chance to work on real projects that will impact future key results of our mission, while receiving constant feedback, and clear goals/directions. This is achieved by providing you support from both mentors, and managers, throughout your entire internship period. At the end of the internship, you will also have the opportunity to share and present the results of your project with the team, as well as others that may be interested in your achievements. RESPONSIBILITIES

Prototype control/estimation algorithms in Python and/or C++ following software development best practices.

Perform literature review across machine learning and control.

Apply ML and/or data science to estimation and control, for evaluating robustness or algorithm development.

Demonstrate algorithm impact in simulation or ideally on vehicle.

Quantify impact on performance through development of metrics and/or test cases.

Bring an Innovation mindset, producing patent(s) and/or research paper(s).

MINIMUM QUALIFICATIONS

Currently pursuing a Masters or PhD degree with focus in Controls, Estimation, Robotics, Machine Learning, or related topics.

Proficiency in Python for prototype development and PyTorch or similar for model training.

Solid understanding of dynamics, control, convex optimization, and machine learning.

Experience with data libraries including jupyter, numpy, scipy, pandas, scikit‑learn, matplotlib, etc.

Ability to organize information and ideas into clear technical reports/presentations.

Passionate about making driving safer.

NICE TO HAVES (optional)

Programming experience in C++.

Demonstrated deployment of algorithms on a real system: vehicle, robot, or other.

Familiarity with robot motion planning methods such as trajectory optimization, sampling‑based planning, or model predictive control.

Experience with SQL, BigQuery, or similar tools for managing large datasets.