Senior ML Platform Engineer – AD​/ADAS

Responsibilities

• Design, build, maintain, optimize and support the ML Platform’s systems and tools for perception, prediction, and planner development, allowing numerous ML engineers to effectively & efficiently iterate on dataset curation, ML modeling, training, evaluation and deployment of ML models into our functionally safe AD/ADAS stack, shipped in millions of Toyota vehicles.
• Develop user-friendly tooling, frameworks and libraries to support the overall ML engineering effort, from ML modeling, to tracking performance metrics and introspecting failure modes.
• Build and maintain efficient dataset generation, cloud training and evaluation pipelines.
• Develop and review code with other ML and ML Platform engineers to facilitate rapid incremental improvements.
• Optimize the current processes, tooling and supporting infrastructure to accelerate the overall ML engineering effort, and contribute to the long term strategy for several of our systems and products.
• Work in a high-velocity environment and employ agile development practices.
• Work in a hybrid workspace, with the requirement to be present in our Palo Alto (USA) office three days per week.

• BSc / BEng (MS / PhD nice-to-have) in Machine Learning, Computer Science, Robotics or related quantitative fields, or equivalent industry experience.
• 5+ years of experience with data structures, algorithms, design patterns, and software engineering best practices.
• 2+ years of experience with UNIX-based systems (Linux or similar), Python, and PyTorch/Tensorflow.
• 2+ years of experience in the full MLOps cycle covering data cleansing, data sampling, data curation, pre-processing, efficient data loading, distributed training, testing, evaluation, deployment, inference optimization and deployment in the cloud and on edge compute platforms.
• Experience with Docker and CI systems such as Git Hub Actions.
• Business-level proficiency in English, able to write technical documents (e.g., for software documentation).

Demonstrates expertise in Machine Learning engineering, including MLOps processes, data management, and cloud deployment. Proficient in developing user-friendly tools and optimizing ML systems for high-performance applications in autonomous driving.

• Machine Learning Engineering
• MLOps Cycle
• Python Programming
• PyTorch/Tensor Flow
• UNIX-Based Systems

• Data Structures
• Algorithms
• Design Patterns
• Software Engineering Best Practices
• Data Cleansing
• Data Sampling
• Data Curation Pre-Processing
• Distributed Training
• Inference Optimization

• Technical Writing
• Collaboration

• Machine Learning
• Autonomous Driving
• ADAS
• Agile Development
• Hybrid Workspace

• Docker
• CI Systems
• Git Hub Actions
• Cloud Platforms