About the job
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. The Oracle Perception team is pioneering the use of large multimodal foundation models (e.g., Gemini) to build a powerful offboard reasoning and data flywheel system. Our core focus is advancing the VLM foundation itself by pushing the boundaries of multimodal pre-training and state-of-the-art post-training (SFT, RL).
Responsibilities
Drive Pre-training & Domain Adaptation: Lead the technical strategy for curating and constructing massive-scale, high-quality multimodal pre-training datasets.
Lead Post-Training & Reasoning Enhancement: Design and implement state-of-the-art fine-tuning (SFT) and Reinforcement Learning (RLHF/RLAIF, DPO/GRPO/PPO) pipelines.
Pioneer the VLM Data Flywheel: Architect the highly scalable inference and evaluation pipelines that leverage these trained Gemini-class models to autonomously source, sample, and autolabel critical edge cases.
Define Training Recipes & Scaling Laws: Conduct rigorous ablation studies to optimize model architectures, token budgets, and loss functions.
Drive Cross-Functional AI Strategy: Act as the principal technical visionary across ML Infra, Perception, Behavior, and AI Foundation teams.
Provide Staff-Level Technical Leadership: Own the long-term technical roadmap for foundation model development. Mentor senior engineers, lead rigorous design reviews, and establish standard-setting engineering practices.
Qualifications
Minimum
Master’s degree in Computer Science, AI, ML, or a related technical field.
8+ years of hands-on experience designing, training, and scaling deep learning models, with at least 3+ years focused deeply on training Large Language Models (LLMs) or Vision-Language Models (VLMs).
Proven expertise in the full lifecycle of Foundation Models: from pre-training data curation (interleaved formats, tokenization) and distributed training to advanced post-training techniques.
Expert-level understanding of training infrastructure and distributed paradigms (e.g., FSDP, Megatron, JAX/Pax) required for training massive models reliably.
Expert-level software engineering fundamentals using Python, PyTorch, or JAX, with a track record of building reliable, highly scalable ML systems.
Proven ability to operate with high ambiguity, define technical roadmaps, and drive complex, multi-quarter technical initiatives across multiple teams in a fast-paced environment.
Preferred
PhD in Computer Science, Artificial Intelligence, or a related field.
Strong publication record in top-tier AI venues (e.g., NeurIPS, ICML, ICLR, CVPR) focusing on foundation models, large-scale training, reinforcement learning, or reasoning.
Deep experience with advanced Reinforcement Learning paradigms applied to language or vision tasks (focusing on improving System 2 thinking, logical deduction, and model alignment).
Demonstrated experience in Data Engineering for Foundation Models at the scale of billions/trillions of tokens (e.g., deduplication, quality filtering, synthetic data generation).
Familiarity with the systemic challenges of multimodal perception in robotics or autonomous driving (e.g., 3D scene understanding, trajectory prediction).
A proven track record of Staff-level impact: influencing product direction, pioneering zero-to-one ML architectures, and multiplying team efficiency through technical leadership.