Senior Research Scientist, Foundation Model (Physical AI)

Qualifications

• Masters or PhD in deep learning in behavior and agent modeling or simulation
• Proficiency in Python
• 3+ years of experience with modern deep learning frameworks
• Prefer JAX or TensorFlow 2; however, Pytorch is acceptable
• Generative modeling
• LLMs or VLMs (e.g., Gemini, Llama, GPT)
• Post-training, incl
• Distillation

Responsibilities

• The mission of the Waymo Applied Research team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of the safely operating Waymo vehicles in dozens of cities and under all driving conditions
• In this hybrid role you will report to a Technical Lead Manager
• Conduct applied foundation model research and development
• Design compelling experiments by training and evaluating large deep learning models
• Present results to peers and leadership
• Write high quality code, unit tests, and documentation
• Transformer models

Full Description

The mission of the Waymo Applied Research team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of the 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.

In this hybrid role you will report to a TechnicalLead Manager.

You will:

• Conduct applied foundation model research and development

• Design compelling experiments by training and evaluating large deep learning models

• Present results to peers and leadership

• Write high quality code, unit tests, and documentation

You have:

• Masters or PhD in deep learning in behavior and agent modeling or simulation

• Proficiency in Python

• 3+ years of experience with modern deep learning frameworks.

Prefer JAX or TensorFlow 2; however, Pytorch is acceptable.

We prefer:

Experience with:

• Generative modeling

• LLMs or VLMs (e.g., Gemini, Llama, GPT)

• Post-training, incl. reinforcement learning

• Distillation

• Transformer models

• Autoencoders and embeddings