Jobs at Wayve

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.

Leads Wayve’s global training strategy for vehicle safety operators and operational teams across the UK, US, Germany, and Japan. Builds scalable curricula, onboarding, certification, compliance, and readiness frameworks; manages regional training leaders; establishes training metrics and reporting; and translates operational, product, and safety requirements into training updates. Partners with Operations, Product, and Safety to ensure consistent, audit-ready workforce readiness in a safety-critical environment.

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 and operate scalable batch and streaming pipelines for multimodal fleet and simulation data. Develop data models, catalogs, indexes, query capabilities, reproducible datasets, and quality validation workflows. Establish standards for lineage, observability, access controls, retention, governance, and cost management. Partner with autonomy, ML, simulation, and safety teams to define schemas, APIs, and data contracts while improving large-scale storage and compute reliability.

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.

Lead the technical strategy, design, and production integration of fault detection and fallback systems for autonomous vehicles. Develop robust robotics solutions, collaborate with machine learning and software teams, guide architecture decisions, and improve overall system safety and reliability. The role also involves mentoring, defining acceptance criteria, analyzing fleet data, and potentially building monitoring, debugging, and real-time systems for large-scale driverless vehicle operations.

Build scalable performance tooling for AI model training and inference across cloud and embedded hardware. Responsibilities include profiling workloads, conducting roofline analysis, identifying layer- and operator-level bottlenecks, predicting latency, memory, utilization, and compute costs, and monitoring regressions. The role requires close collaboration with model, compiler, runtime, platform, and hardware teams to establish performance targets and translate measurements into actionable recommendations.

Own and extend Wayve’s machine learning compilation pipeline from model checkpoint to deployable embedded bundles across NVIDIA TensorRT and Qualcomm QNN targets. Design compiler passes, quantization and precision-typing systems, graph partitioning, legalization, regression testing, and benchmarking infrastructure. Partner with model and training teams to ensure compilability, accuracy, and latency across architectures and SoCs. Provide technical direction and mentorship for compiler engineering.

Research and develop foundation models for general-purpose robotics, including multimodal architectures, scalable training methods, data strategies, and post-training techniques. Build distributed pipelines using large-scale video and robot-interaction datasets, evaluate models on real robots, and collaborate with robotics, hardware, and engineering teams. The role involves frontier research across vision-language-action models, world models, imitation learning, reinforcement learning, and self-supervised learning, with opportunities for publications and ownership of a new embodied AI research program.

Build and operate cloud infrastructure, CI/CD pipelines, build systems, Infrastructure-as-Code, automation, developer tooling, and observability platforms. Scale build agents, runners, artifact storage, and containerized workloads; troubleshoot infrastructure and deployment issues; support incident response, root cause analysis, secure delivery, reliability, and cloud cost efficiency.

Develop and deploy deep learning systems for autonomous driving, including personalized and collaborative driving behaviors. Build model architectures, data pipelines, evaluation frameworks, and production deployment pathways. Curate real-world and synthetic data, improve closed- and open-loop performance, collaborate across autonomy teams, lead technical initiatives, and mentor engineers.

Leads large-scale data curation, enrichment, and rare-event mining for autonomous vehicle machine learning systems. Builds workflows for model introspection, out-of-distribution detection, robust evaluation, and driverless operation. The role spans model engineering, infrastructure, compute, and data, requiring ownership of distributed ML systems, large-scale inference, and collaboration across research and engineering teams.

Build and operate scalable data pipelines for autonomous-driving machine learning workflows. Responsibilities include ingesting, transforming, curating, validating, and monitoring large real-world and synthetic datasets; improving pipeline performance and reliability; supporting scenario mining and model evaluation; and collaborating with ML engineers, Data Corpus teams, AI Platform, and external partners.