ML Platform Engineer, AD/ADAS
Core
Design, build, and maintain ML Platform systems and tools to enable ML engineers to iterate on dataset curation, modeling, training, evaluation, and deployment of ML models for perception, prediction, and motion planning in the AD/ADAS stack.
Role type
ML Platform Engineer (AD/ADAS)
Builds
Scalable and efficient ML training and evaluation pipelines, user-friendly tooling, frameworks, and libraries for the AD/ADAS stack.
Domain
Autonomous driving, Advanced Driver Assist Systems (AD/ADAS), Machine Learning
Deliverable
production ML models
Required skills
Data structures and algorithms, UNIX-based systems (Linux), Python, PyTorch/Tensorflow, MLOps cycle (data cleansing, sampling, curation, pre-processing, distributed training, testing, evaluation, deployment), Docker, CI systems (GitHub Actions)
Preferred skills
Apache Spark, Airflow, Flyte, Flink, Ray, Rust, C++, Bazel, SIMD/SIMT parallelism, GPU programming, Terraform, AWS, Kubernetes, Google Big Query, Snowflake, AWS Redshift, deep-learning model optimization, self-driving, robotics, computer vision, motion planning
Technologies
PyTorch, TensorFlow, Docker, GitHub Actions, Apache Spark, Airflow, Flyte, Flink, Ray, Rust, C++, Bazel, Terraform, AWS, Kubernetes, Google Big Query, Snowflake, AWS Redshift
Responsibilities
Design and optimize ML Platform systems for perception, prediction, and planner development; Develop tooling and libraries for ML engineering; Build and maintain dataset generation and cloud training pipelines; Review code with ML engineers; Optimize processes and infrastructure for ML engineering; Work in a high-velocity environment using agile practices.
Seniority
Mid-level, hands-on IC