Role Description Waymo’s simulator is one of the most complex virtual environments ever built. It blends deterministic logic, physical dynamics, and state-of-the-art Generative AI to create a training ground for the Waymo Driver. The Simulator Evaluation team faces the ultimate data challenge: How do you mathematically prove that a virtual world is "real"? We are seeking visionary machine learning engineers and researchers to architect the scalable deep learning systems, novel data workflows, and eval tools that power our research roadmap. In this role, you will pioneer the machine learning and generative vision paradigms required to define and measure the realism of our multimodal world models. Your work will define the state of the art for autonomous simulation, directly steering our research trajectory and the capabilities of the Waymo Driver. You will: - Lead the design, development and deployment of cutting-edge evaluation approaches to assess realism of state-of-the-art multimodel world models and generative systems for simulation use cases at Waymo. - Architect and implement robust and scalable machine learning pipelines for tuning, evaluating, and deploying large-scale discriminator models for the purposes of simulator realism evaluation. - Evaluate open-source and production-ready video generation techniques that measure realism (e.g. temporal stability, multi-modal consistency, geometric discrepancy, condition following, etc.). - Apply vision language models to evaluate semantic understanding and controllability across our world simulation products. - Collaborate with research teams across Waymo and Alphabet to integrate advancements in 4D world modeling and generative AI into production systems. - Mentor engineers on the team and provide technical guidance on architecture and execution. Qualifications - Bachelor's, Master's, or PhD in computer science, machine learning, robotics, or a related field. - Five or more years of experience in machine learning engineering or applied deep learning, supported by a portfolio of shipped products or peer-reviewed publications. - Proficient programming skills in Python and hands-on experience with modern machine learning frameworks such as Jax, Flax, or PyTorch. - Experience designing and implementing evaluation frameworks for complex systems or machine learning models. Requirements - Track record of training large-scale generative models (diffusion models, flow matching, vision language models, etc.). - A PhD and demonstrated success delivering machine learning products focused on 3D generative models, world models, or video generation. - Experience simulating sensor data, including camera, lidar, and radar, or modeling semantic scenes. - Experience developing autonomous systems, robotics software, or autonomous vehicle simulations. - Experience training and optimizing large-scale models on GPU or TPU clusters for efficient production serving. - Professional experience writing C++ for high-performance production systems. Benefits - Waymo employees are also eligible to participate 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 listed below. 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. $213,000 — $263,000 USD

Role Description The Machine Learning Operations (MLOps) Engineer is responsible for building and maintaining the infrastructure that supports the development, deployment, and monitoring of machine learning models. - Work closely with data scientists and other MLOps engineers to streamline workflows, automate processes, and ensure the scalability and reliability of ML systems in production environments. - Ensure that the team is able to offer machine learning model predictions at scale. - Deliver reliable, scalable ML systems that enable teams to move models from experimentation to production quickly and safely, while maintaining high standards for performance, security, and operational excellence. Qualifications - Bachelor’s degree in a related field or the equivalent through a combination of education and related work experience. - Ability to collaborate and solve problems effectively with excellent cross-team collaboration and communication skills. - Experience with Git. - Familiarity with cloud platforms such as AWS, Azure, or GCP, including deploying and operating services in cloud environments. - 3 years minimum related work experience. - Proficiency in Python and experience applying software engineering best practices (version control, testing, and code reviews). - Strong problem-solving skills, attention to detail, and ability to troubleshoot production issues. - Working knowledge of model governance concepts, including model versioning, experiment tracking, reproducibility, and rollback strategies. Requirements - Experience with Databricks, Azure Pipelines, and major cloud platforms (AWS, Azure, and GCP). - Experience with Docker, Kubernetes, SQL, and enterprise scale data management practices. - Working knowledge of Generative AI technologies and their operational considerations. Benefits - Competitive compensation and benefits to support your health and well-being. - Qualifying associates can enroll themselves and/or their eligible dependents in medical and prescription drug coverage. - Participation in the H&R Block Retirement Savings Plan (401(k) Plan). - Access to the Employee Assistance Program, (virtual) fitness center programs, and the associate discount program. - Automatic enrollment in Business Travel Accident Insurance. - Receive Associate Tax Prep benefit. Company Description Since 1955, we have been leaders in tax preparation, financial services, and small business solutions. With 70,000 associates and 9,000 retail tax locations across North America, Australia, Ireland, and India, we have helped millions of clients and countless communities. If you embrace challenges as opportunities, value winning as a team, and seek to make a meaningful difference, join us on our journey.

• Own the architecture of Workiva’s AI platform and core AI services • Shape how machine learning, Generative AI, and agentic systems are integrated across products • Lead the move from early adoption to production-grade, enterprise-ready systems • Define standards for model serving, retrieval, evaluation, governance, and platform reliability • Lead the design of enterprise agentic systems, including orchestration, workflow execution, memory, and multi-agent coordination • Design and evolve Retrieval-Augmented Generation capabilities for enterprise content and knowledge workflows • Establish evaluation methods and quality frameworks for Generative AI applications • Assess emerging AI technologies and guide adoption strategy for Workiva’s platform • Influence technical direction across teams, products, and platform domains • Mentor Staff and Senior Engineers and help raise the technical bar across the organization • Partner closely with Product, Security, Infrastructure, and Architecture leaders • Align teams around a shared vision for scalable, secure AI at Workiva • Lead secure AI platform design, including authorization, runtime isolation, governance, auditability, and compliance • Establish best practices for AI safety, model governance, and customer data protection • Ensure AI systems meet enterprise expectations for availability, resiliency, observability, and operational support • Design for fault tolerance and operational excellence in regulated, security-conscious environments

• Evolve and maintain our Kubeflow, Feast and Spark-on-Kubernetes infrastructure, ensuring it can handle the increasing complexity of both traditional ML and the new wave of AI. • Design the internal tools, APIs, and abstractions that empower distributed teams to own their entire lifecycle—transitioning our data culture from "centralised bottleneck" to "self-service excellence." • Collaborate with embedded Data Science teams to adapt software engineering best practices (CI/CD, versioning, testing) to ML-specific workflows, raising the bar for production-grade AI across the company. • Drive MLOps best practices and define the specific lifecycle requirements for LLMOps, ensuring a frictionless journey from experimental notebooks to robust, production-grade solutions by providing top-notch deployment frameworks and automation. • Partner closely with our Infrastructure and Data squads to break silos and integrate ML artifacts into our global Data Catalog, Privacy, and Governance frameworks. • Inspire and empower others by genuinely caring for your own wellbeing and your colleagues.