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.

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.

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.

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.

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.

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.

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.

Lead large-scale training and improvement of Gaia, Wayve’s video world model for autonomous driving. Design experiments, influence model architecture and training strategy, improve synthetic scenario generation, and deliver production-grade training systems. Collaborate with research, simulation, applications, and infrastructure teams while providing technical leadership through mentorship, reviews, and direction-setting.

Develop and ship end-to-end machine learning models for autonomous driving behaviors across L2-L4 systems. Own data curation, training, evaluation, experimentation, and on-road validation. Improve model performance through iterative training cycles while addressing real-world safety, quality, and deployment constraints. The role may involve reinforcement learning, transformer networks, automotive systems, model compression, and embedded deployment.

Own the full AI model release lifecycle, from training and evaluation through on-road testing and fleet deployment. Define promotion gates, manage regressions and rollbacks, improve release processes, build dashboards and KPIs, influence automated tooling, and coordinate ML engineering, data science, software platform, testing, and product teams. Serve as the central contact for release status, risks, timelines, and continuous improvement.

Build and operate secure, reliable, and cost-effective AI platform infrastructure for language models and agentic workflows. Responsibilities include developing runtimes, services, developer tooling, observability, governance controls, deployment automation, and reusable platform capabilities. Partner with Security, IT, and engineering teams to integrate and scale the platform, support adoption, document best practices, and troubleshoot issues. The role also owns projects end to end and participates in on-call support.

Designs and evolves scalable data pipeline, orchestration, and inference infrastructure for autonomous driving data platforms. Responsibilities include developing DAG-based workflow systems, tooling, shared libraries, batch inference capabilities, and high-throughput processing workflows. The role focuses on reliability, performance optimization, resource prioritization, incident response, and collaboration with partner teams to support large-scale data ingestion, processing, evaluation, and machine learning operations.