Artificial Intelligence Jobs

Wayve · London

Lead a newly dedicated Runtime Platform team building the application frameworks, middleware, IPC, performance and observability foundations that let Wayve's onboard AI Driver run in real time. The role is people-first, combining coaching, hiring and roadmap ownership with technical direction across latency, profiling, real-time scheduling and embedded Linux, partnering with vehicle software, hardware and OEM programme teams.

Wayve · London

Lead functional safety activities for an end-to-end autonomous driving system, building Item Definitions, running HARA/FMEA/FTA analyses, and shaping safety concepts and architectures across vehicle platforms and third-party systems. Define verification and validation, including vehicle-level testing, and construct structured safety cases with claims, arguments and evidence to support certification.

Faculty · London

Owns and develops product and service design as a core discipline within the Applied AI division, delivering billable client engagements and anchoring project deliverables. Builds executive relationships with client leadership, defines skill and progression frameworks, and partners with business development to shape multidisciplinary teams. Balances strategic delivery with building an entrepreneurial design function.

Faculty · London

Lead and grow a banking practice within an applied AI consultancy, shaping go-to-market strategy and building relationships with C-suite banking leaders. You will drive complex AI transformation programmes from strategy through software delivery, generate revenue through pitches and demos, and mentor multidisciplinary teams. The role blends commercial ownership with deep sector expertise in a regulated industry.

Wayve · London

Post-train, distill and evaluate GAIA-family large video diffusion world models for synthetic driving data, covering embodiment and pose transfer. Own large-scale GPU generation pipelines and land synthetic data into the driving-model training stack, working between ML research and platform engineering.

Wayve · London

Builds virtual vehicle models using physics-based and data-driven techniques, validating them against on-road data and converting them into efficient software components. Runs track and partner experiments to improve model accuracy, and integrates models into simulation, hardware-in-the-loop and correlation pipelines across robotics and product teams.

Wayve · London

Owns the engineering release process for autonomous driving models, defining branching strategies, release cadence, configuration management and quality gates. Reviews model, architecture and code changes against intended outcomes, assesses evaluation results and builds CI/CD tooling and observability to deliver ML systems reliably at scale.

Wayve · London

Lead a small team iterating on Wayve's end-to-end L2 driving model, balancing hands-on Python coding and experiments with directing priorities. You'll root-cause on-road issues and fix them through data recipes, model architecture, evaluation and release readiness, working across teams on model and vehicle interface problems.

Wayve · London

Train, debug and improve computer vision and 3D perception models for driver assistance, working across the full ML lifecycle from data and training to evaluation and iteration. The role spans latency-constrained on-car perception and large-scale offline work with lidar, maps and 3D reconstruction to build auto-labelling data flywheels.

Wayve · London

Develop next-generation generative world models and planners such as GAIA, simulating complex driving environments for faster training and scalable deployment. Work spans diffusion and transformer architectures, latent compression, context pruning and inference-latency optimisation, with closed-loop agent interaction and sim-to-real evaluation. The role combines ML research, multi-modal modelling and production deployment, including mentoring and publishing.

Wayve · London

Owns validation strategy for ADAS and L2 autonomy functions, translating product, safety and regulatory requirements into test scenarios, metrics, coverage and acceptance criteria. Works hands-on with Python evaluation pipelines, simulation and SQL-based data analysis, spanning simulated and physical test environments. Open at senior IC or manager level, with close collaboration across Product, Safety and Release stakeholders.

Wayve · London

Lead large-scale training and post-training for Gaia, Wayve's world model that predicts future driving video and runs closed-loop with the driving stack. Own model architecture decisions, push autoregressive rollouts toward longer, more stable generation, and make the model deployment-ready. Partner with research, applications, simulation engineering and cloud teams, mentoring and setting technical direction.

Wayve · London

You'll define and manage the technical architecture for integrating Wayve's AI Driver into OEM and Tier 1 vehicle platforms, covering software–vehicle interfaces, sensors, middleware, ECUs, timing, diagnostics and validation. The role sits between core AI architecture and customer programs, turning ambiguous requirements into interface contracts, ownership boundaries and verifiable specifications.

Wayve · London

Owns the architecture, strategy and integrity of safety cases for robotaxi and OEM ADAS products, defining top-level claims, argumentation structures and evidence traceability. Sits independently from Engineering and Product, providing expert challenge and oversight across AI/ML, systems, validation and operational safety. Requires deep safety-case and systems engineering expertise in safety-critical domains.

Wayve · London

Builds ground-truth datasets and computer vision classifiers that describe complex driving scenes across Wayve's data corpus, drawing on the World Foundation Model to surface rare scenarios and performance gaps. Day to day spans defining annotation tasks and quality criteria, curating datasets, training and evaluating models in PyTorch, and integrating classifiers into enrichment infrastructure for corpus-wide inference.