Artificial Intelligence Jobs

Wayve · London

Lead the Applied Methods Data Science team, partnering with the Autonomy organisation to turn experimental and observational analysis of real and simulated driving into evaluations and insights. Set three-, six- and 12-month roadmaps, review technical proposals for statistical rigour, hire and grow data scientists, and build tooling to scale the team's capability.

NVIDIA · Reading

Acts as the primary technical contact for academic and research partners, embedding NVIDIA's frameworks and software stack into large-scale AI and HPC workloads. Drives joint research projects, delivers workshops and trainings, and feeds researcher needs back to engineering. Requires deep LLM lifecycle expertise across multi-node systems and involves around 30% travel.

NVIDIA · Italy, Remote

This role involves owning the fabric operations of NVIDIA's Cloud Partner (NCP) fleets, including NVLink/NVSwitch partition management, switch software and firmware lifecycle, and fabric validation. You will work closely with customers, partners, and cross-functional teams to ensure reliable and efficient network operations in large GPU estates.

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.

NVIDIA · United Kingdom

Acts as the primary technical contact for academic and research partners, embedding NVIDIA's frameworks and software stack into large-scale AI and HPC workloads. Drives joint research projects, delivers workshops and trainings, and feeds researcher needs back to engineering. Requires deep LLM lifecycle expertise across multi-node systems and involves around 30% travel.

NVIDIA · Courbevoie, France

Engages directly with customers and partners to architect and validate large-scale GPU-accelerated HPC/AI platforms, spanning hardware handover through Day 2 production stability. Works tool-agnostically across upstream Kubernetes, KubeVirt, Slurm and observability stacks, owning cluster-wide validation, burn-in and fleet reliability targets.

NVIDIA · United Kingdom

Engages directly with customers and partners to architect and validate large-scale GPU-accelerated HPC/AI clusters, spanning hardware handover through Day 2 production stability. Works tool-agnostically across upstream Kubernetes, KubeVirt, Slurm, Prometheus/Grafana and Cumulus/SONiC, owning burn-in testing, fleet reliability and enablement runbooks.

NVIDIA · Germany

Engages directly with customers and partners to architect and validate large-scale GPU/HPC clusters, owning full-solution validation and Day 2 production stability at fleet scale. Works tool-agnostically across upstream Kubernetes, KubeVirt, Slurm, Prometheus/Grafana and Cumulus/SONiC, acting as hands-on technical leader and trusted advisor for assigned accounts.

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

Owns the end-to-end architecture and technical roadmap for Remote Assistance, the real-time bridge between Wayve's AI Driver and human operators, spanning onboard vehicle integration, offboard services and operator HMI. Defines the vehicle-operator contract, fallback/MRM behaviour and latency budgets, and leads cross-org execution across autonomy, safety, platform and product teams to support L4 operation.

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.