Jobs at Waymo

Build and lead the Vehicle Realism Platform for Waymo’s autonomous-driving simulator. Design scalable architectures, improve simulator reliability and reproducibility, bridge vehicle subsystems with simulation infrastructure, and deliver realistic simulation solutions across hardware platforms. Collaborate with robotics, onboard, hardware, evaluation, and simulator teams. The role requires backend development, production monitoring, systems programming, C++, and experience building products in distributed environments.

Owns identity and access management processes for Waymo’s global extended workforce. Develops tooling requirements, scripts, and automation with Engineering; audits access, enforces least-privilege controls, mitigates security risks, and leads future-state access programs. Partners with cybersecurity, tool owners, engineers, and business leaders to translate security standards into efficient workflows, manage technical dependencies, and improve access governance.

Graduate software engineering interns will analyze autonomous driving behaviors, build automated pipelines for undesirable-event response, improve behavior-detection signals, and quantify driving performance. The role involves Python or C++, data analysis using SQL or Python, and data-focused experimentation. Preferred experience includes machine learning model training and evaluation, agentic systems, C++, and large-scale distributed systems. The internship is hybrid and onsite.

Lead a team of 5–6 engineers building Waymo’s marketplace platform and infrastructure. Develop ML infrastructure for training and deploying models that support real-time pricing, rider-vehicle matching, positioning, and supply-demand optimization. Build experimentation, monitoring, analysis, and simulation systems for evaluating deployed models and improving marketplace decisions. The role requires technical leadership, people management, backend programming, and production ML experience.

Develop learning-based behavior policies for autonomous vehicles by integrating multimodal perception semantics, building simulation benchmarks and counterfactual suites, and inspecting 3D driving trajectories. The intern will apply reinforcement learning, imitation learning, trajectory forecasting, and deep learning methods while collaborating on Waymo's autonomous driving technology.

Lead the Site Reliability Engineering team responsible for Waymo’s release and pipeline infrastructure. Own observability, incident response, capacity planning, on-call operations, software hardening, playbooks, and continuous reliability improvements. Investigate novel production events, contribute to system architecture, and partner across engineering organizations to improve large-scale software delivery systems supporting autonomous vehicles. Manage organizational growth, including hiring and developing additional engineers.

Develop and improve machine learning systems for autonomous driving perception. Responsibilities include creating scalable ML methods, analyzing real-world system behavior, diagnosing root causes, enabling distributed fine-tuning, preventing production regressions, monitoring deployed models, building automated debugging tools, and developing evaluation metrics and recipes. The role requires Python or C++, PyTorch or JAX, and at least three years of machine learning or computer vision experience.

PhD intern will improve causal and predictive pricing models using feature engineering, regularization, time series estimation, and loss quantification. The role involves applying causal inference, econometrics, predictive modeling, Python, and SQL to support pricing decisions and operational efficiency at an autonomous driving company.

Drive cross-functional execution of onboard autonomous driving and machine learning programs. Manage schedules, risks, dependencies, milestones, and stakeholder communications across engineering, research, product, and release management. Contribute to strategic planning and technical roadmaps for foundation models, resolve roadblocks, and report program progress and KPIs to leadership. The role requires technical program management experience in software environments and familiarity with machine learning models, training pipelines, and evaluation frameworks.

Software engineering intern developing features that improve autonomous driving behavior. The role involves integrating and validating large machine learning models in real-time production systems, collaborating with onboard engineers, and applying software engineering, robotics, computer vision, and motion-planning techniques.

Analyze real-world and simulated autonomous-driving data, validate safety and reachability metrics against expert evaluations, investigate edge cases, and optimize model trade-offs. Collaborate cross-functionally to integrate quantitative safety signals into event triage and release-readiness workflows.

Software Engineering Interns investigate autonomous driving behavior using logs and large-scale data, identify root causes, develop perception and driving behavior improvements, enhance evaluation capabilities, and address challenging long-tail driving scenarios. The role involves collaborating across teams and contributing to Waymo’s Level 4 autonomous driving technology.

Conduct exploratory research, evaluate theories statistically, present findings to focus groups, and design and implement software. The intern will work on autonomous driving technology while collaborating with technical teams. Required qualifications include current PhD enrollment in computer science or a related technical field, Python programming fluency, statistical knowledge, and basic machine learning knowledge.

Analyze deep multimodal perception models, validate performance using autonomous vehicle datasets and simulations, train and fine-tune neural networks, assess data quality, and improve data pipelines. The role requires Python programming, deep learning experience, and knowledge of modern neural architectures for computer vision or multimodal perception. Preferred qualifications include research publications, robotics or autonomous-driving perception experience, and large-scale system evaluation expertise.

Leads technical architecture and integration for building automation, building operating systems, charging infrastructure, and cloud platforms. Serves as Master Systems Integrator, establishes BAS/BOS standards, oversees controls programming and network design, resolves critical outages, leads root cause analysis, and manages automation technicians and integration engineers. Partners with IT, hardware, facilities, fleet operations, and vendors to deliver scalable smart-building solutions.

Develop reliable Android components and user experiences for autonomous vehicle interiors, including pickup and drop-off, infotainment, displays, audio, and climate controls. Collaborate with designers, product managers, and vehicle software engineers to deliver safety-sensitive features. Lead technical designs, mentor engineers, manage the software lifecycle, and optimize performance for embedded systems.

Manage external regulatory data reporting programs: intake regulations, develop policies/procedures, coordinate Legal/Product/Engineering, ensure data collection quality and tooling, respond to regulator requests, and analyze trends to improve processes.

MBA finance interns will collaborate across teams, build long-term financial models, conduct financial and external research, derive strategic insights, support business decisions, and present recommendations to senior leadership. The role requires strong financial modeling, financial statement knowledge, strategic thinking, and an MBA focused on finance, strategic management, or technology. Preferred experience includes management consulting, valuation, rideshare, automotive, autonomous vehicle, software, or technology industries.

Lead software engineering for autonomous vehicle pick-up and drop-off behaviors. Design cross-component solutions, build quantitative on-road evaluation methods, develop foundational frameworks, analyze large datasets, collaborate with product and onboard teams, mentor engineers, and address challenging long-tail driving scenarios. The role requires substantial complex-systems engineering and management experience, with preferred expertise in autonomous vehicles, robotics, and production machine learning.

Leads the design and implementation of zero-touch EV charging automation for an autonomous vehicle fleet. Integrates robotic charging systems, vehicle controls, charging hardware, cloud platforms, APIs, and data systems. Develops automated tests, CI/CD quality processes, standardized commissioning workflows, and virtualization opportunities. Coordinates product, engineering, commissioning vendors, and external partners while overseeing hardware-in-the-loop and in-vehicle testing.