Artificial Intelligence Jobs in London

Wayve · London

Lead the technical architecture and roadmap for onboard localisation, designing and productionising state-estimation systems that fuse GNSS, IMU and wheel odometry for accurate vehicle pose estimates. The role spans algorithm design, prototyping, validation and deployment on real-time vehicle platforms, with hands-on technical leadership and mentorship across robotics, calibration, controls and sensor teams.

Wayve · London

Develops embedded software connecting an AI Driver to vehicle steering, braking and propulsion systems, working hands-on with benches and vehicles. Involves MATLAB/Simulink development, drive-by-wire actuation logic, CAN/CAN-FD diagnostics, OTA updates and ADAS/HMI interfaces, collaborating with OEMs, suppliers and controls engineers.

ElevenLabs · United Kingdom

Build and operate production-grade abuse detection and guardrail systems, focusing on free-tier abuse, fraud, and bot farming across a multimodal platform. Own the safety infrastructure end to end, from architecture and APIs to data pipelines, monitoring, and SLIs/SLOs, working with ML and full-stack engineers to move research models into production.

ElevenLabs · United Kingdom

Owns large-scale distributed web crawling systems that discover, fetch and extract data from billions of pages to feed frontier AI models. Work spans content extraction from messy HTML, web-scale deduplication, recrawl and freshness strategies, politeness and rate limiting, plus tooling for researchers to request and explore crawled data.

Wayve · London

Owns the data strategy behind an end-to-end autonomous driving AI platform, spanning the full learning cycle from foundation model training to production programmes. Leads a multidisciplinary data organisation, deciding what data matters and building the flywheel from real-world signals through discovery, curation and enrichment. Focuses on finding rare, safety-critical scenarios in very large datasets using embeddings, semantic search and model-driven selection.

Wayve · London

Hands-on role optimising inference for edge accelerators and GPUs so large transformer models run efficiently on low-cost, low-power in-vehicle compute. You'll profile model graphs, compiler/runtime behaviour and kernel execution, implement optimisations like fusion, scheduling and custom kernels, and build benchmarking and regression tests across NVIDIA Orin/Thor and Qualcomm targets, working closely with model, platform and deployment teams.

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

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

Lead large-scale data enrichment and curation for the Core Model Safety team, mining hard and rare events and building workflows across pre/mid-training, reward modelling and self-supervised labelling. Design and train out-of-distribution detection mechanisms for robust driverless operation, working at the intersection of model engineering, infrastructure, compute and data.

Wayve · London

Owns reliability of large-scale GPU, Kubernetes and distributed AI training infrastructure, turning recurring operational problems into durable engineering solutions. Improves observability, automation, deployment safety, capacity planning and incident learning, and builds GitOps delivery with Argo CD or Flux. Works across AI Platform, Compute & Data Platform and Cloud Infrastructure teams.

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 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.

Wayve · London

Build and operate a globally distributed Data Transfer Hub moving petabytes of video, LiDAR and sensor data per day between Wayve and partners such as Uber, Stellantis and Nissan. Write pipeline logic in Python, manage infrastructure with Terraform, and use Flyte, Kafka and Azure for orchestration, event-driven lifecycle tracking and storage. The small, high-autonomy team owns production reliability, observability and cost efficiency, with an out-of-hours on-call rotation once the service is established.

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.