Jobs at Waymo

Develop multimodal and multitask perception models for autonomous driving, including 3D detection, tracking, segmentation, occupancy detection, and foundation models. Train and evaluate machine learning systems on large-scale Waymo driving data, conduct applied computer vision research, and optimize models for onboard and offboard hardware while collaborating with engineering and research teams.

Lead systems engineering work to define and measure critical driving behaviors, develop metrics, provide verification requirements, anticipate risks, and drive cross-functional projects to improve autonomous vehicle system performance and reliability.

Design and build infrastructure and tooling to diagnose software non-determinism across autonomous driving systems. Investigate and resolve non-deterministic execution patterns across Waymo codebases, applying strong C++ programming, algorithms, data structures, and systems debugging skills. Preferred experience includes statistical data analysis, anomaly detection, tracing tools, concurrency and floating-point determinism, and graph execution frameworks.

Lead NVH strategy across full vehicle development: set requirements and targets, drive design and integration, perform CAE (FEA/SEA) simulations, plan and run lab and road NVH tests, lead root-cause analysis and countermeasures, and collaborate with internal teams and OEMs to ensure quiet, comfortable vehicle cabins.

Develop and optimize machine learning models for Waymo’s autonomous driving perception systems. Responsibilities include implementing LoRA and QLoRA fine-tuning in JAX/Flax, building knowledge-distillation pipelines, validating quantization and model optimization techniques, benchmarking latency, and evaluating perception performance across large autonomous-driving datasets. The intern will collaborate with model optimization, hardware, and latency teams while working on multi-task vision transformers and onboard deployment.

Build Python infrastructure for real-time monitoring, diagnostics, and automated verification of autonomous vehicle communication networks. Develop validation pipelines, security monitoring, and diagnostic workflows for CAN, CAN-FD, and Ethernet systems. Analyze network performance, troubleshoot integration issues, and collaborate with systems, network, and vehicle security engineers. The role also involves AI-assisted software development and testing across vehicle platforms and test environments.

Leads Waymo’s Collision Avoidance Testing Team, including hiring, goal-setting, technical direction, and career development. Partners with planning, perception, systems, safety, simulation, and tooling teams to develop scalable, maintainable, and extensible autonomous-vehicle behavioral testing frameworks. The role requires building scalable testing programs, guiding architecture for long-tail behavioral testing, and delivering safety-critical engineering impact.

Lead end-to-end electrical, mechanical, and software integration for autonomous vehicle platforms. Drive system bring-up, multidisciplinary root-cause investigations, subsystem requirements and interface definitions, validation experiments, prototype debugging, and technical coordination with vendors and manufacturing partners. The role requires strong systems engineering leadership, programming for diagnostics or automation, and expertise in vehicle architectures, safety systems, communications, and laboratory instrumentation. Approximately 10% travel is required.

Build and evolve mission-critical backend infrastructure and commercialization tools for Waymo’s autonomous vehicle fleet. Collaborate with Product, Security, and engineering teams to develop secure systems, including vehicle configuration, secure parts transactions, key provisioning, and security infrastructure. Apply backend engineering, microservices, cryptography, PKI, authentication, encryption, and access-control expertise to solve business-critical problems.

Serve as the on-site People Team partner for Waymo’s London office. Responsibilities include supporting leaders, adapting global culture programs, addressing UK employment compliance, advising on investigations and performance processes, supporting local hiring, analyzing HR metrics, and representing London employees’ needs across the global People organization.

Leads network operations for resilient, high-availability infrastructure supporting autonomous fleet and enterprise systems. Manages engineers, technical staff, vendors, major incidents, ITIL service processes, deployments, network device maintenance, documentation, and continuous improvement. Provides technical leadership for routing, infrastructure design, event correlation, and automation initiatives. Requires extensive network operations or engineering experience, people management, and high-bandwidth infrastructure expertise, with preferred experience in Ansible, Python, Terraform, BGP, and complex routing.

PhD intern will develop uncertainty-aware algorithms for transformer-based multi-task classifiers, implement model and data changes end-to-end, evaluate experiments, and ensure modeling implementations are correct. The role requires strong Python programming, deep learning framework experience, machine learning theory, transformer debugging with TensorBoard, and software development best practices.

Design and maintain large-scale backend services that deliver configurations and features to autonomous vehicle fleets. Build commercialization tools, robust fleet rollout processes, and safe feature launches using evaluation, A/B testing, and gradual deployments. Collaborate with Product, Operations, Data Science, and engineering teams to ensure system safety, reliability, and effective cross-functional execution.

Serve as primary privacy counsel for Germany and the EU, advising on GDPR, ePrivacy, BDSG, privacy by design, data minimization, sensor data, AI/ML initiatives, cross-border transfers, and data governance. Engage with European Data Protection Authorities, lead incident responses and DPIAs, support audits and regulatory inquiries, and advise on emerging technology legislation and policy. Partner with product, engineering, public policy, trust and safety, and regulatory teams to manage privacy compliance and risk.

Develop onboard C++ software governing autonomous vehicle interactions with traffic controls. Validate changes through large-scale simulation, evaluate safety and compliance metrics, and produce clean, tested, production-grade code for a safety-critical autonomous driving system.

Lead and contribute to Waymo’s planet-scale ML data infrastructure and inference platform. Partner with modeling teams to manage data quality, distributions, freshness, feature stores, and ML flywheels. Mentor engineers, lead complex cross-functional projects, and support scalable data stores, model hosting, bulk and online inference, and high-QPS production services.

Analyze large-scale autonomous driving and simulation data to develop safety and driving-quality metrics, statistical models, predictive features, and machine-learning methodologies. Build repeatable data analysis and reporting pipelines in collaboration with engineering teams. The role requires pursuing a quantitative PhD, programming proficiency in Python or R, and strong statistical theory. Preferred qualifications include machine learning, SQL or C/C++, high-dimensional data experience, research publications, foundation-model expertise, and autonomous-driving or generative-AI internship experience.

Leads vehicle product quality through APQP, PPAP, design reviews, FMEAs, control plans, validation, and manufacturing launch. Partners with OEMs and cross-functional teams to ensure specification compliance, investigate defects using 8D and related methods, analyze warranty and assembly data, and drive corrective actions and continuous improvement. Establishes safe-launch protocols and lifecycle quality monitoring for vehicle hardware, firmware, and components. The role requires 20% travel to manufacturing sites.

Translates machine learning data requirements into labeling policies, creates golden datasets, manages labeling queues and vendor teams, analyzes data quality, handles edge cases, and improves annotation workflows through automation and cross-functional coordination.

Train and fine-tune multi-task transformer models for autonomous driving using JAX/Flax on TPUs. Build data and evaluation pipelines, conduct rigorous model evaluations, contribute production-quality code, and communicate findings through design documents and presentations. Collaborate with engineering, data science, and evaluation stakeholders.