176 jobs
PhysicsX London
Owns deployment of ML models and engineering surrogates (deep learning on CAE/CFD/FEA data, forecasting, anomaly detection) into customer production environments, building scalable training and inference pipelines with Python, PyTorch, Kubeflow and fastAPI. Leads scoping and architecture, mentors engineers, and travels to customer sites roughly 3-4 weeks per quarter.
PhysicsX New York, United States
Builds and automates high-fidelity electromagnetic models across low- and high-frequency applications, from chip and package design to EMI/EMC, using tools like HFSS, Maxwell and CST. Works at the intersection of CAE and data science, generating simulation data to train ML surrogates, collaborating with customers and mentoring junior engineers.
Wayve London
Leads complex, multi-team programmes across Level 2 and Level 4 autonomous driving products, translating product milestones into integrated cross-team schedules and delivery plans. Reviews technical progress, backlog health and system-level evidence to assess milestone readiness, managing dependencies and critical-path activities across engineering, product, safety and validation. Improves lifecycle-planning practices and tooling to surface risk earlier and deliver more predictably.
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.
ElevenLabs London
Builds and operates the platform every product team ships on, running and scaling Kubernetes clusters including GPU clusters for inference and training across cloud and on-prem. Sets up compute, networking, permissions and access, hosts data systems, and builds tooling and automations to make infrastructure self-serve, working hands-on with research, product, security and safety engineers.
ElevenLabs United Kingdom
Owns the data infrastructure behind frontier AI models, building large-scale pipelines for collecting, filtering and transforming training datasets. Also trains classifiers, quality filters and labeling models, and designs curation strategies like deduplication and quality scoring. Builds tooling so researchers can explore and train on massive datasets quickly.
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.
Wayve London
Owns the technical vision and architecture for a platform that lets model engineers deploy, run, evaluate and improve ML models over petabytes of driving data and large GPU fleets. Combines hands-on backend engineering in Python with cross-team technical leadership, mentoring senior engineers and leading a small team of 2-3.
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
Owns shared onboard runtime foundations for autonomous vehicles: application frameworks, communication middleware, lifecycle management, health monitoring and observability. Day to day involves C++ development, end-to-end latency and performance profiling across OS scheduling, memory hierarchy and thread synchronisation, plus porting the stack to new operating systems and compute platforms.
Wayve London
You'll design, build and operate shared infrastructure for model access, agent runtimes and observability, taking projects from problem definition through technical design, deployment and ongoing operation. Working with security, IT and engineering teams, you'll create governed access layers, reusable tooling and standards so teams can run AI agents in production without one-off infrastructure.
Wayve London
Develops and maintains Wayve's Core Simulation system in modern C++ and Python, integrating driving-stack components, simulated robot systems and vehicle-platform capabilities. Initially focused on high-scale open-loop evaluation, the role involves debugging on-road versus simulated behaviour, improving reproducibility, observability and automated testing, and profiling simulator performance across a shared codebase.
Wayve London
Sets technical strategy and roadmap for fault detection and fallback systems in an autonomous driving stack, designing and training detection mechanisms for robust driverless operation. Leads integration into the shared driving stack, aligning ML, inference optimisation and software teams, and mentors through architecture reviews. Works with large-scale training and fleet data in a small, high-impact team.
