Software Engineer - ML Infrastructure

Highlights

• Build core data systems powering autonomous edge perception across fleets.
• Develop training orchestration spanning cloud and on-premise compute infrastructure.
• Deploy improved perception models from cloud pipelines back to edge devices.

Ideal candidate

An infrastructure-minded builder who loves enabling perception engineers and thrives partnering with customers to ship reliable, mission-critical autonomy systems at scale.

Requirements

• Own and operate core model training and dataset management infrastructure for perception
• Partner with perception engineers to improve training, evaluation tooling and SDKs
• Integrate frontier models for data curation, annotation, augmentation and semantic search
• Build observability for ML lifecycle including training monitoring, evaluation reports and model registry
• Strong proficiency in modern Python with exposure to Rust and PyO3/Maturin toolchain
• Experience with deep learning frameworks like TensorFlow or PyTorch and distributed training
• Experience with MLOps orchestration and observability tools such as Modal, Flyte, Airflow, Dagster, W&B, MLflow