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.

In this hybrid role, you will report to a Staff Research Scientist.

You will:

• Design and implement cutting-edge models and algorithms for spatial-temporal visual reasoning in autonomous driving scenarios, utilizing large language and vision models (LLM/VLM).
• Develop and maintain robust training and evaluation pipelines for VLM models, focusing on metrics relevant to decision-making rationale and free-form question answering.
• Collaborate with ML engineers and researchers to integrate visual reasoning capabilities into product systems.
• Lead the effort to set up proper large-scale rationale/QA data and establish rigorous evaluation metrics for visual reasoning tasks.
• Explore and implement advanced techniques, including Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), to optimize model performance for complex visual reasoning challenges.
• Analyze and interpret large-scale sensor data to identify key challenges and opportunities for improving LLM/VLM performance in real-world driving environments.

You have:

• 5+ years of experience in Machine Learning, with a focus on large-scale model development (LLM, VLM, or similar foundation models).
• Proven expertise in visual reasoning, scene understanding, or related areas within autonomous driving, robotics, or computer vision.
• Strong coding proficiency in Python and deep learning frameworks (e.g., Jax, TensorFlow, PyTorch).
• Hands-on experience with model training, evaluation, and deployment in a production environment.
• Master's degree in Computer Science, Electrical Engineering, or a related field, or equivalent practical experience.

We prefer:

• Experience with advanced training techniques, such as Supervised Fine-Tuning (SFT) or Reinforcement Learning (RL) for language/vision models.
• Familiarity with large-scale data curation and quality assurance processes for multimodal datasets (e.g., rationale/QA data).
• Background in autonomous vehicle perception, motion planning, or decision-making systems.
• Publications in top-tier machine learning or computer vision conferences (e.g., NeurIPS, ICML, CVPR, ICCV, ECCV).
• PhD in a relevant field.