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Waymo is looking to improve its autonomous driving technology by enhancing the perception system's ability to 'see' and understand the world around the self-driving car, addressing real-world problems and aiming for safer mobility.

Requirements

• 5+ years of experience in developing and deploying computer vision models in a professional or research setting.
• Demonstrated expertise in deep learning frameworks such as PyTorch or TensorFlow/JAX, and strong proficiency in Python and C++.
• Proven ability to lead complex technical projects and drive research ideas from conception to production deployment.
• Deep understanding of modern computer vision architectures and their practical application.
• Experience with large-scale data, model training, and evaluation pipelines for computer vision tasks.
• Experience in the autonomous vehicle (AV) or robotics industry, particularly with real-time on-device perception systems.
• Experience with Multi-Modal Large Language Models (MLLMs), diffusion models, and advanced reasoning techniques.

Responsibilities

• Drive the research, design, and implementation of state-of-the-art computer vision and deep learning models for autonomous driving, with a focus on semantic understanding.
• Lead the development of novel computer vision algorithms to solve critical real-world perception problems in a safety-critical production environment.
• Push the boundaries of computer vision model performance, efficiency, and robustness across diverse operating conditions.
• Collaborate with ML infrastructure and Onboard teams to deploy and optimize models on in-vehicle hardware with real-time constraints.
• Mentor junior engineers and contribute to the technical vision and roadmap for the Perception team's CV stack.
• Stay current with the latest academic and industry research in Computer Vision and Machine Learning and apply relevant findings.

Other

• Master’s degree or PhD in Computer Science, Engineering, or a related technical field with a specialization in Computer Vision or Machine Learning.
• Strong communication and collaboration skills in a fast-paced, cross-functional environment.
• Proven track record of publications at top-tier computer vision or machine learning conferences (e.g., CVPR, ICCV, ECCV, NeurIPS).
• Experience with model optimization techniques such as quantization, pruning, or knowledge distillation.
• Familiarity with distributed training and inference optimization on GPU/TPU clusters.