Machine Learning Engineer II - Autonomous Driving Training Infrastructure
Core
Design and operate data, training, and evaluation pipelines to scale autonomous driving ML models for commercial deployment.
Role type
ML Infrastructure Engineer (Autonomous Driving)
Builds
Data and training pipelines for autonomous vehicle decision-making systems
Domain
Autonomous driving / Robotics
Deliverable
production ML models
Required skills
C++, Python, PyTorch, Linux, distributed systems, data pipeline architecture, GPU scheduling, CI/CD
Preferred skills
Go, Rust, Ray, Kubeflow, Airflow, MLflow, Weights & Biases, PyTorch DDP/FSDP, DeepSpeed, Spark, Parquet
Technologies
PyTorch, Linux, Kubernetes (implied by containerized), Spark, Parquet, Ray, Kubeflow, Airflow, MLflow, Weights & Biases
Responsibilities
Transition ML models from concept to commercial scale; Architect and operate data, training, and evaluation pipelines across cloud and cluster environments; Design and maintain data and metadata stores for training workflows
Seniority
Mid-level, hands-on IC
