Role Description The Perception team builds the system which learns the spatial-temporal representation and their semantic meanings of the surrounding environment of the autonomously driving vehicle (ADV), i.e., the system that “perceives” the world around the car. We work jointly with downstream teams on the optimization and integration into the Waymo Driver. We conduct our own research to address real-world problems and collaborate with research teams at Alphabet. We have access to millions of miles of driving data from a diverse set of sensors, enabling engineers like you to: - Develop methods for efficiently and continuously learning from large scale real-world data. - Develop models and model training at scale. - Analyze real-world behavior and develop systems for handling the complexities of interacting with the real-world. - Optimize models for our onboard and offboard hardware. You will: - Design VLM/LLM model architecture and drive strong alignment between model architectures and hardware architectures. - Optimize model performance for on-device use cases (memory, power, compute constrained environments). - Engage directly with research, software engineering, hardware engineering, and product teams to deliver end-to-end solutions. Qualifications - 7+ years of experience in Machine Learning, with a focus on large-scale model development (LLM, VLM, or similar foundation models). - Proven expertise in low-latency on-device inference techniques and a deep understanding of hardware acceleration. - Extensive experience with deep learning frameworks (e.g. PyTorch, JAX) and large-scale model training. - A track record of operating effectively under ambiguity, setting direction amid rapidly evolving research and technical constraints. - Experience applying large language models or foundation models in complex, safety-critical domains (e.g., autonomy, robotics, or other high-reliability systems). - Master's degree in Computer Science, Electrical Engineering, or a related field, or equivalent practical experience. Requirements - Familiarity with large-scale data curation and quality assurance processes for multimodal datasets. - 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. Benefits - Participation in Waymo’s discretionary annual bonus program. - Equity incentive plan. - Generous Company benefits program, subject to eligibility requirements. Salary Range The expected base salary range for this full-time position across US locations is: $298,000 — $368,000 USD Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.

• Own business metrics (e.g. churn reduction, AI attach rate, seller productivity) and form an opinionated point of view on what we should build next to move them • Deeply understand our users - how they work, where they struggle, what they need - and advocate for building the right things, not just building things right • Define success criteria for the intelligent systems you ship, measure whether they're working for users, and iterate until they are • Develop Python backend services and APIs that connect AI capabilities with modern web applications • Build and maintain the data infrastructure (Snowflake, dbt, Airflow) that powers our AI products • Stay abreast of the latest advancements in LLMs, agent architectures, evaluation frameworks, and AI-assisted development

• Build and operate the ML lifecycle platform. Own the tooling that makes model development reproducible and production-ready, with MLflow (or comparable systems) at the center: experiment tracking, model registry, artifact and metadata management, and versioned, repeatable training and inference pipelines. • Own CI/CD and deployment for ML workloads. Build automated pipelines that move models from notebook to production safely, including packaging, containerization, automated testing and validation, staged rollouts, and rollback. • Make models observable and reliable in production. Stand up monitoring for model and service health, including latency, drift, data-quality, and cost signals, with alerting and clear runbooks so issues surface and resolve quickly. • Build the cloud-native foundations. Contribute to and manage containerized workloads on Kubernetes and codify infrastructure with infrastructure-as-code tooling such as Terraform, keeping environments consistent, secure, and reproducible. • Establish sensible guardrails. Implement infrastructure-level governance for ML systems, including access controls, deployment policies, and auditability, partnering with security and compliance to align with our risk and regulatory requirements. • Enable and mentor the teams you support. Define repeatable patterns and shared services that reduce friction for data and application teams, provide technical guidance and mentorship to junior engineers, and contribute to the direction of dv01's MLOps practices.

• Build and improve the orchestration layer that manages state transitions, context sharing, and intent routing across vendor and internal LLM frameworks in a distributed conversational environment. • Develop production-grade Python services that bridge advanced AI and ML capabilities with reliable customer-facing products. • Drive well-scoped ML projects from design through delivery, balancing technical trade-offs and collaborating across teams. • Contribute to system design, coding standards, and AI/ML development best practices across the team. • Partner with engineers and cross-functional stakeholders to build secure, scalable, and high-performing AI-enabled experiences. • Participate in design reviews and help ensure features meet Coinbase standards for security, compliance, and performance.