Senior Machine Learning Engineer, Multimodal Perception (LLM/VLM)

The Semantics team is a specialized subgroup within the Perception organization at Waymo. Our mission is to bring the immense reasoning power and innate world knowledge of massive foundation models directly onto the Waymo Driver. We focus on building an onboard multi-task, multimodal perception model designed to tackle highly complex and unpredictable "long-tail" scenarios.

You Will:

• Architect and train large-scale, onboard ML perception models that are instrumental to ensuring vehicle safety and regulatory compliance.
• Drive cross-functional collaboration to engineer robust, high-reliability training pipelines within a dynamic, rapid-delivery environment.
• Leverage deep computer vision expertise to design novel, custom architectures from first principles to solve complex perception challenges.
• Contribute to a vibrant and positive team culture where diverse skill sets and backgrounds are valued. Support the growth of junior engineers and foster a high-performing, collaborative team environment.

You Have:

• BS or MS in Computer Vision, Machine Learning, Robotics, or a related field.
• 4+ years of applied industry experience in autonomous vehicles, robotics, or complex ML systems.
• Fluency in Python or C++, with deep hands-on expertise in PyTorch or Jax for matrix manipulation and module implementation.
• Deep understanding and proven practical experience with model distillation frameworks and quantization techniques for real-time compute constraints.
• Demonstrated hands-on experience building, training, or deploying Multimodal Foundation Models or Vision-Language Models (VLMs).

We Prefer:

• PhD in Computer Vision, Machine Learning, Robotics, or a related field.
• Hands-on experience managing and optimizing large-scale teacher-student training loops.
• A proven track record of successfully deploying Vision-Language queries in highly constrained, real-time environments.
• Experience with large-scale distributed training, Parameter-Efficient Fine-Tuning (PEFT), or Reinforcement Learning from Human Feedback (RLHF) for Foundation Models and VLMs.
• Deep expertise in long-context temporal reasoning for sequential decision-making or complex video understanding.
• First-author publications in premier computer vision and machine learning conferences, such as CVPR, NeurIPS, ICCV, or ECCV.