Top Tech Jobs & Startup Jobs in London

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.