Jobs at Wayve

Own the analytical layer for autonomous-driving model evaluation and release decisions. Analyze simulation and on-road experiment data, improve performance metrics and scoring methods, identify measurement gaps, apply statistical rigor, and translate investigations into actionable recommendations. Partner with Data Science, Validation, Product, engineering, and OEM teams to strengthen evaluation coverage, ground truth, quality investigations, and weekly model-release feedback loops.

Develop and optimize high-performance embedded Linux software for autonomous vehicle systems. Investigate latency and performance bottlenecks, instrument code with profiling tools and metrics, evaluate performance technologies, establish benchmarks, and optimize critical code paths. Collaborate across engineering teams to diagnose issues, prioritize improvements, and enhance runtime efficiency and reliability across Wayve’s development fleet.

Manage the daily operation of Wayve’s Leonberg office, including workplace services, hospitality, events, suppliers, contractors, onboarding, health and safety, and administrative support. The role is fully on-site and serves as the primary contact for workplace matters, ensuring a safe, welcoming, efficient, and high-quality office experience.

Build, operate, and improve Azure-focused cloud infrastructure supporting software development, testing, and production. Develop Infrastructure-as-Code, maintain CI/CD pipelines, manage containerized workloads, implement observability, troubleshoot incidents, improve security and reliability, and automate deployment workflows. Collaborate with engineering teams to deliver scalable, cost-efficient platforms.

Lead post-training and fine-tuning of Wayve’s Gaia world model, improving long-horizon autoregressive stability, steerability, inference reliability, and deployment readiness. Collaborate across research, simulation, applications, and infrastructure teams to deliver measurable driving-model impact. Provide technical leadership through architecture and training decisions, mentorship, code review, experimentation, and targeted data curation.

Conduct research and develop embodied AI systems for autonomous driving, including world models, planners, reinforcement learning, reward modeling, geometric foundation models, multimodal learning, and simulation. Evaluate scaling, generalization, and sim-to-real transfer while creating benchmarks for long-horizon prediction and driving performance. Collaborate across machine learning, robotics, simulation, and deployment teams.

The Applied Scientist will develop AI systems for autonomous driving, focusing on machine learning, simulation, robotics, and real-world deployment, while advancing embodied AI technologies.

As an Applied Scientist, you'll develop AI systems for autonomous driving, focusing on machine learning, simulation, and robotics innovations. Responsibilities include world modeling, reinforcement learning, spatial intelligence, and cross-embodiment robotics, collaborating within a research team to push advancements in embodied AI.

The Applied Scientist will develop next-gen AI systems for autonomous driving, focusing on machine learning, simulation, and robotics, advancing embodied AI technologies.

Develop physics-based and data-driven vehicle dynamics models, integrate them into software and simulation workflows, and validate performance against experimental and on-road data. The role includes mathematical modelling, numerical analysis, software optimisation, experimental test planning, performance analysis, and cross-functional collaboration with robotics, simulation, and product teams supporting autonomous driving systems.

Provides commercial legal counsel across supplier, SaaS, services, OEM, and technology agreements. Advises on intellectual property, AI, data usage, privacy, employment, international expansion, and vehicle fleet operations. Develops scalable legal processes, templates, playbooks, and governance while explaining legal risks to business stakeholders and supporting strategic transactions and product deployments.

Build scalable machine learning pipelines, data and training infrastructure, distributed processing workflows, and reusable software systems for foundation-model research. Own codebase health, testing, reliability, maintainability, and performance across ML workflows. Collaborate with researchers and engineers to translate model and experiment requirements into robust systems supporting large datasets, large models, multimodal data, video, robotics, and embodied AI.

Design and integrate mechanical systems for vehicle-mounted sensors and compute platforms. Responsibilities include developing prototypes, test rigs, jigs, fixtures, drawings, assemblies, BOMs, and GD&T documentation; partnering with suppliers on manufacturing; performing risk assessments; and supporting validation, installation, quality, and field-team knowledge transfer.

Build, operate, and improve Azure-focused cloud infrastructure supporting software development, testing, and production. Implement Infrastructure as Code, maintain CI/CD pipelines, manage containerized workloads, and improve monitoring, observability, security, reliability, and cost efficiency. The role also involves troubleshooting incidents, conducting root cause analysis, partnering with engineering teams, and automating infrastructure and deployment workflows.

Establishes and improves configuration and change management across the engineering lifecycle. Responsibilities include configuration identification, baselining, change control, status accounting, audits, requirements traceability, Automotive SPICE process support, compliance evidence, tooling, automation, metrics, and guidance for engineering teams.

Develop and integrate vehicle-facing robot software for autonomous-driving platforms. Responsibilities include vehicle software bring-up, embedded hardware integration, ROS2 development, system debugging, sensor validation, diagnostics, reliability tooling, and architectural improvements. The role collaborates with hardware, safety, controls, calibration, and reliability teams and supports bench, HIL, and on-vehicle testing at international locations.

Develop and maintain Wayve’s embedded Linux distribution, kernel and device drivers for autonomous-vehicle fleets. Build reliable C++ software with strong observability, diagnostics, fault tolerance, and remote debugging capabilities. Collaborate with product, project, hardware, supply chain, and engineering teams. Lead engineering best practices across testing, documentation, tooling, mentorship, and collaboration. Apply expertise in Yocto, boot systems, IPC middleware, robotics frameworks, and embedded systems.

Develop actionable insights for AI engineering teams using experimental and observational analyses of real and simulated driving. Design metrics, experiments, and off-road measurements; investigate model training and inference bottlenecks; validate improvement hypotheses; and communicate findings that guide product priorities, safety, functionality, and performance. The role requires strong SQL, statistical analysis, data science, and experimentation skills, with machine learning and distributed-computing experience preferred.

Build, operate, and improve large-scale data ingestion pipelines supporting autonomous driving AI systems. Responsibilities include debugging pipeline failures, handling corrupt and inconsistent data, optimizing Spark jobs, managing orchestration and retries, improving throughput and reliability, reducing operational toil, and supporting downstream annotation, data science, training, and evaluation teams.

Own the technical vision and architecture for a petabyte-scale data enrichment platform supporting machine-learning deployment, inference, evaluation, monitoring, annotation, and retraining. Build reliable backend services, APIs, workflows, and self-service capabilities across large GPU fleets. Lead cross-team architecture and delivery, establish platform metrics, improve reliability and cost efficiency, and mentor engineers.