Description
Software Engineer, ML Platform (Internship)
Team: AD/ADAS
Location: Ann Arbor, MI
Commitment: Intern
Workplace Type: hybrid
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 team builds the machine learning training and deployment ecosystem for AD/ADAS. You will be embedded within the Automated and Assisted Driving team and collaborate closely with Autonomy ML engineers working on Perception and Planning. Our mission is to design scalable, reliable, and cost-effective ML infrastructure that enables rapid iteration and deployment of high-quality ML models, from large-scale data curation and distributed training, to push-button deployment in production. This work will support the modeling and analysis of large scale human driving data, including analysis of driver monitoring and human factors—such as driver behavior, variability, and physiological and cognitive state (e.g., user ID, eye tracking, emotional state, or other human sensing data)—to better understand interactions between humans and automated driving systems.
Who We Are Looking For
We are seeking motivated software interns with a strong interest in ML systems and MLOps. The ideal candidate has hands-on experience training machine learning models and is interested in improving the infrastructure that enables ML research and production at scale.
This role is well suited for candidates who want to work at the intersection of software engineering and machine learning. Interns in this position will contribute to well-scoped infrastructure projects and help identify and address bottlenecks in dataset creation, distributed training, and model evaluation pipelines. In addition, the role may involve developing frameworks and analytical pipelines that incorporate human physiological and variability in human behavioral data to support modeling and evaluation of real-world driving systems.
The position offers close collaboration with senior engineers and ML practitioners, regular technical feedback, and the opportunity to influence core platform components that are used daily by AD/ADAS ML engineers. Successful candidates will gain exposure to production-grade ML infrastructure and make measurable improvements to the reliability, scalability, and efficiency of the ML development lifecycle. This includes applying computational methods to analyze interactions between human physiological responses, behavior, and autonomous system performance in safety-critical environments.
RESPONSIBILITIES
- Own and drive well‑defined projects within our ML platform and training infrastructure
- Analyze performance, scalability, and reliability bottlenecks in production ML workflows
- Improve observability of training and evaluation pipelines through profiling, logging, and telemetry
- Design and integrate MLOps tools that improve developer productivity and system reliability
- Develop robust integration tests to improve platform stability
- Quantify and validate improvements through systematic benchmarking and experimentation
- Implement large scale exploratory data analysis frameworks to study human driving behaviors, and human physiological responses in real-world driving interactions.
- Document technical designs and findings, and present progress and results to the team
MINIMUM QUALIFICATIONS
- Currently pursuing a BSc, Master’s or PhD in Computer Science, Computer Engineering, or a related field
- Expert proficiency in Python and experience with PyTorch or similar ML frameworks
- Experience with containerization and deployment technologies (e.g., Docker)
- Experience building scalable data processing or ML workflows using systems such as Kubernetes, Airflow, Flyte, or similar platforms
- Experience designing, implementing, and maintaining software systems or research tooling
- Proficiency with version control systems (e.g., Git)
- Familiarity with benchmarking, experimentation, and performance evaluation methodologies
NICE TO HAVES
- Experience with distributed training frameworks (e.g., PyTorch Distributed, Horovod)
- Knowledge of cloud infrastructure and resource management (e.g., AWS, GCP, Azure)
- Experience designing ML systems or infrastructure for research or production environments
- Background in autonomous driving, robotics, or large‑scale perception systems
- Familiarity with C++ or performance‑critical systems programming
- Strong technical writing and presentation skills
Our Commitment
・We are an equal opportunity employer and value diversity.
・Any information we receive from you will be used only in the hiring and onboarding process. Please see our privacy notice for more details.
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