ML Engineer, Foundation Model Evaluation

The mission of the Waymo AI Foundations team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of safely operating Waymo vehicles in dozens of cities and under all driving conditions. As part of our work, we also initiate and foster collaborations with other research teams in Alphabet. AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation.

This role follows a hybrid work schedule and you will report to a Senior Research Scientist.

You will:

• Develop and extend cutting-edge research in robotics and machine learning to advance state-of-the-art methodologies for evaluating the quality, safety, and realism of embodied AI agents
• Partner within and across organizations to land disruptive and innovative tech in production
• Work with a variety of state-of-the-art Foundation Models
• Drive model development through defining evaluation and benchmarks
• Implement and extend large large scale data and evaluation pipelines.

You have:

• Masters degree in Computer Science, Machine Learning, Robotics, similar technical field of study, or equivalent practical experience
• Proficiency in Python
• Familiarity with one of the modern deep learning frameworks (e.g. Pytorch, JAX, Tensorflow)
• Prior work in an industrial or research setting developing methodologies for the evaluation of ML models

We prefer:

• Strong hands-on SWE skills, able to design, implement, and extend large distributed pipelines
• Track record of publications in top-tier conferences or leading open source projects in the related fields
• Proficiency in C++
• Experience in AV planning and related research
• Experience in labeling and curating data for ML eval and training

((Remote jobs only - Please note that Waymo may not be able to employ remotely in all locations. Please speak with your recruiter about your preferred location for remote work when you begin the interview process.))