Senior ML Engineer - Multi-Modal Perception & Autonomy

Waymo; Mountain View (CA); On-site; USD 213,000 - 263,000; Full time

Job summary

Waymo in Mountain View is seeking a Perception-focused MLE on the Special Vehicle Compliance team. The role centers on multi-modal sensor fusion, multi-task deep learning architectures for vehicle semantics and signal detection, and high‑resolution vision backbones for robust autonomous operation.

In this hybrid role, you will collaborate with the Technical Lead Manager, build scalable data pipelines, and optimize models for onboard inference.

Qualifications

• 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.

Responsibilities

• Architect, train, and optimize multi-task deep learning models 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.

Skills

Multimodal learning

Sensor fusion

PyTorch / JAX

Transformer architectures

Active learning

Tools

PyTorch

JAX

Job description

Waymo in Mountain View is seeking a Perception-focused MLE on the Special Vehicle Compliance team. The role centers on multi-modal sensor fusion, multi-task deep learning architectures for vehicle semantics and signal detection, and high‑resolution vision backbones for robust autonomous operation.

In this hybrid role, you will collaborate with the Technical Lead Manager, build scalable data pipelines, and optimize models for onboard inference.