Software Engineer, ML Platform… at Woven by Toyota · Ann Arbor

Summary: Woven by Toyota is enabling Toyota’s transformation into a mobility company, focusing on human-centric innovation in mobility. They are seeking motivated software interns with a strong interest in ML systems and MLOps to contribute to infrastructure projects and improve ML workflows. 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 - Document technical designs and findings, and present progress and results to the team - 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 - Document technical designs and findings, and present progress and results to the team Required 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 Preferred Qualifications: - 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 Required Skills: Python, PyTorch, MLOps Important Skills: Docker, Kubernetes, Git Nice-to-Have Skills: Technical writing, Presentation skills