190 jobs
PhysicsX Singapore
Builds physical foundation models for industrial engineering, scaling training across multi-GPU cloud compute and turning research prototypes into robust, optimised implementations. Works closely with research scientists, simulation engineers and customers, owning research work-streams and translating results into reusable libraries, tooling and products.
PhysicsX Singapore
Builds and operates a SLURM-on-Kubernetes HPC platform with 304 B200 GPUs, split evenly between platform engineering and hands-on support for researchers. Work covers cluster reliability, containerised job environments, high-performance storage and networking, and debugging distributed training and data generation workloads.
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
Builds full-stack geospatial services in Python and TypeScript, turning global road-network and navigation data into products for an autonomous driving platform. Owns data pipelines with PySpark and Databricks, route-planning applications, and map data preparation for training and simulation, deploying services on Azure and Kubernetes end to end.
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
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.
Wayve London
Lead a team of ML engineers and applied scientists building synthetic data from generative world models like GAIA-3, turning them into a production engine for training-grade driving experience. Own the loop end to end, from checkpoint and large-scale GPU inference to artefacts landing in driving-model training, while setting technical direction on rig transfer, pose transfer and controllability.
