Senior Machine Learning Engineer, Multimodal Perception

The Special Vehicle Compliance team develops the multi-modal perception, semantic reasoning, and driving intelligence that enables the autonomous vehicle to safely interact with high-stakes road actors. We are actively advancing our systems toward data-driven learned policies and end-to-end architectures, powered by large-scale closed-loop data engines.

Role overview: Perception-focused MLE role dedicated to multi-modal sensor fusion, multi-task deep learning architectures for vehicle semantics and signal detection, dynamic high-resolution vision backbones, and large-scale automated data engines.

In this hybrid role, you will report to the Technical Lead Manager of the Special Vehicle Compliance team.

You will:

• Architect, train, and optimize multi-task deep learning models (PyTorch / JAX) across multi-modal sensor streams.
• Build automated data mining pipelines, active learning loops, hard-example curation, and auto-labeling systems.
• Develop high-resolution vision architectures and spatial-temporal transformer backbones.
• Leverage multimodal foundation models for automated data curation, synthetic edge-case generation, and failure triage.
• Profile and optimize models for efficient onboard accelerator inference.

You have:

• 2–5+ years training and deploying production vision or multi-modal deep learning models.
• Experience with multi-modal sensor fusion (Camera + LiDAR + Audio), multi-task learning (MTL), transformer architectures, and PyTorch / JAX.
• Experience building large-scale data curation pipelines, active learning loops, and auto-labeling systems.
• Fluency with modern AI developer tools and foundation model workflows for fast prototyping.