Staff Machine Learning Engineer (Vision-Language Foundation Models)

• In the Oracle Perception team, our mission is to build the ultimate cognitive engine for autonomous driving
• We are pioneering the use of large multimodal foundation models (e.g., Gemini) to build a powerful offboard reasoning and data flywheel system
• We are moving beyond traditional perception to true scene understanding and driving actions—building offboard models that can comprehend complex driving problems, predict object/scene dynamics, and deduce driving paths with logical rationale
• 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), we are creating models capable of rich, reasoning-based autolabeling at a massive scale
• This closed-loop data engine directly powers the training and evolution of Waymo’s real-time onboard models
• If you are passionate about defining VLM training recipes, scaling laws, and unlocking complex reasoning via RL, this is your opportunity to redefine the foundation of autonomous driving
• In this hybrid role, you will report to a Senior Staff Technical Lead Manager
• Drive Pre-training & Domain Adaptation: Lead the technical strategy for curating and constructing massive-scale, high-quality multimodal pre-training datasets. Define data mixture strategies to instill deep, Waymo-specific driving intuition and physics-grounded understanding into foundation models without catastrophic forgetting
• Lead Post-Training & Reasoning Enhancement: Design and implement state-of-the-art fine-tuning (SFT) and Reinforcement Learning (RLHF/RLAIF, DPO/GRPO/PPO) pipelines. Drastically improve the model’s instruction-following and complex reasoning capabilities (e.g., Chain-of-Thought, spatial-temporal reasoning, and driving rationale prediction)
• 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, directly accelerating the onboard perception models
• Define Training Recipes & Scaling Laws: Conduct rigorous ablation studies to optimize model architectures, token budgets, and loss functions. Establish best practices for scaling multimodal training efficiently on large GPU/TPU clusters
• Drive Cross-Functional AI Strategy: Act as the principal technical visionary across ML Infra, Perception, Behavior, and AI Foundation teams. Drive consensus on the data flywheel architecture and embed VLM reasoning capabilities seamlessly into the broader autonomous vehicle stack
• 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 from advanced prototyping to production deployment

Benefits

• Medical, dental, and vision insurance for employees and dependents
• Employee assistance programs focused on mental health
• Personalized workplace adjustments for diverse needs and abilities, including physical, mental, and neurodivergent considerations
• Access to mental health apps
• Onsite wellness centers
• Medical advocacy program for transgender employees
• Counseling services
• Second medical opinion for you and your loved ones
• Support programs including menopause benefit
• Competitive compensation
• Regular bonus and equity performance grant opportunities
• Generous 401(k) and regional retirement plans
• Annual cross-company compensation review and pay equity analysis
• 1-on-1 financial coaching
• Fertility and growing family assistance
• Parental leave and baby bonding leave
• Survivor income benefit
• Elder care and support
• Backup childcare
• Caregiver leave
• Paid time off, including vacation, bereavement, sick leave, parental leave, disability, and holidays
• Jury duty leave
• Military leave
• Hybrid work model with remote work opportunities also available
• Educational reimbursement
• Peer learning and coaching platform
• Donation matching programs
• Volunteer hours
• Employee resource groups
• Internal community groups and local culture clubs
• Inspiring spaces to work, recharge, and collaborate with fellow Waymonauts
• On-site meals and snacks
• Fitness centers, massage programs, and ergonomic support
• On-demand fitness, wellbeing, and cooking classes
• Commuter benefits- 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
• 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)
• Expert-level understanding of training infrastructure and distributed paradigms (e.g., FSDP, Megatron, JAX/Pax) required for training massive models reliably
• 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
• Expert-level software engineering fundamentals using Python, PyTorch, or JAX, with a track record of building reliable, highly scalable ML systems
• Master’s degree in Computer Science, AI, ML, or a related technical field
• 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