Machine Learning Engineer, Sensor Pipelines

Software Engineering builds the brains of Waymo's fully autonomous driving technology. Our software allows the Waymo Driver to perceive the world around it, make the right decision for every situation, and deliver people safely to their destinations. We think deeply and solve complex technical challenges in areas like robotics, perception, decision-making and deep learning, while collaborating with hardware and systems engineers. If you’re a software engineer or researcher who’s curious and passionate about Level 4 autonomous driving, we'd like to meet you. 

The Sensor Pipelines team applies sensor fusion and ML approaches to address critical challenges in Perception; like detections of Collisions, Antagonistic Behaviors like Vandalism, Sensing Occlusions, etc. Our work involves cutting-edge research (Gen AI) to solve real-world problems and requires close collaboration with onboard teams across Alphabet. We have access to millions of miles of driving data from a diverse set of sensors, enabling engineers like you to develop sophisticated models and techniques at scale.

This role follows a hybrid work schedule and reports to a Technical Lead Manager.

You will:

• Apply sensor fusion, machine learning techniques to build multi-modal sensor fusion architectures and spatial-temporal representation learners to solve real-world challenges
• Develop and deploy machine learning models, including using Generative Artificial Intelligence (Gen AI) system, and non-ML systems to solve those challenging problems
• Develop data mining, labeling, training and eval pipelines to support the onboard development
• Collaborate and work in partnership with product, infra and research teams across Waymo

You have:

• Bachelors in Computer Science or a similar discipline, or an equivalent amount of deep learning experience
• 3+ years experience in Machine Learning and/or Computer Vision
• Experience with C++ and Python
• Experience with ML frameworks like PyTorch or JAX

We prefer:

• MS or PhD Degree in Machine Learning, Robotics, Computer Science or a similar discipline
• Publications at top-tier conferences like CVPR, ICCV, ECCV, ICLR, ICML, ICRA, IROS, RSS, NeurIPS, AAAI, IJCV, PAMI
• Github repositories or Tech Blogs of LLMs/ VLMs