Machine Learning Jobs

• New York, United States
• Hybrid · 2–3 days in office
• Mid-level

Builds high-fidelity CFD models from geometry clean-up and meshing through simulation and post-processing, using Star-CCM+, OpenFOAM or Fluent and parametric CAD tools. Works alongside data scientists and ML engineers to generate simulation datasets for deep learning surrogates, running jobs on cloud and on-premise HPC, with occasional international travel to customer sites.

Star-CCM+ · OpenFOAM · Fluent +4

• London

Owns the product capability for post-training LLMs, turning research methods into deployable model improvements for customers. Builds reliable training harnesses, synthetic-data pipelines and evaluation workflows, applying SFT, preference optimisation, RLHF/RLAIF and verifier-based approaches. Works closely with Research and platform teams, with a roadmap spanning SaaS, VPC and air-gapped deployments.

PyTorch · LLMs · SaaS +1

• London
• Remote
• Lead
• Permanent

Own 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. Define the vehicle–operator contract, fallback/MRM behaviour and latency budgets, and drive cross-org execution across autonomy, safety, platform and product teams feeding into the L4 safety case.

HMI

• London
• Hybrid
• Senior
• Permanent

Set the technical strategy and roadmap for fault detection and fallback systems in an autonomous driving stack, designing and training detection mechanisms that enable safe driverless operation. Lead integration into the shared driving stack, working closely with machine learning, inference optimisation and software engineering partners, and mentor across teams. The role sits in a small, high-impact Core Model Safety team with access to large-scale training and fleet data.

• United Kingdom
• Remote
• Senior

Develops performance optimizations in deep learning frameworks using JAX, contributing to JAX core components and driving peak performance on NVIDIA hardware. Works with AI researchers to build future-proof models and tooling that bridges numerical computing, simulation and deep learning research. Requires C/C++, Python and machine learning framework internals expertise.

JAX · PyTorch · TensorFlow +4

• Cambridge
• Hybrid
• Senior

Develops algorithms and optimizations for NVIDIA's LPX inference and compiler stack, building high-performance runtime and compiler components and mapping large-scale neural network workloads onto future hardware. Involves LLVM/MLIR work, graph transformations, scheduling and memory/layout optimizations, plus close collaboration with hardware architects on co-design.

LLVM · MLIR · TensorFlow +3

• Cambridge, United States
• Hybrid · 3 days in office
• Senior · manages people

Lead computational drug design programs from hit identification through to candidate nomination, applying AI-first approaches and shaping the ML drug design engine alongside ML scientists. Roles span Program Lead or Computational Chemistry Lead, with some positions managing 2-3 computational chemists. Hybrid working in Cambridge, MA, three days a week in office.

REINVENT

• Cambridge, United States
• Hybrid · 3 days in office
• Lead

Lead DMPK strategy across drug discovery projects, designing screening cascades and mechanistic experiments while integrating PK, PD and toxicology data for dose and safety predictions. Work with AI/ML platform teams to optimise ADME models and author regulatory DMPK sections, in a hybrid Cambridge, MA role.

Python · R

• London
• Hybrid
• Permanent

Deliver bespoke, production-grade machine learning systems for government and national security clients, spanning the full ML lifecycle from scoping to deployment. Work with Python, Scikit-learn, TensorFlow or PyTorch, plus cloud platforms and Docker/Kubernetes, while advising stakeholders and setting standards for deploying ML at scale.

Scikit-learn · TensorFlow · PyTorch +5

• London
• Hybrid
• Mid-level
• Permanent

Deliver bespoke, production-grade machine learning systems for government and national security clients, spanning the full ML lifecycle from scoping and architecture to deployment. Work with Python, Scikit-learn, TensorFlow or PyTorch, plus cloud platforms and Docker/Kubernetes, while advising stakeholders and setting standards for deploying ML at scale.

Scikit-learn · TensorFlow · PyTorch +5

• London
• Hybrid · 3 days in office
• Permanent

Partner directly with engineering organizations to design, build, and deploy AI-powered software development workflows using Codex, working hands-on in Python to create prototypes, evaluation harnesses, integrations, and production accelerators. The role combines deep technical engineering with customer leadership, guiding teams from use-case selection through production rollout and scaled adoption while feeding deployment insights back to Product and Research.

Codex · OpenAI

• London
• Hybrid
• Permanent

Optimises large language model training performance, focusing on throughput and accelerator utilisation. Writes low-level CUDA and Triton kernels, works on distributed training strategies, and builds profiling tools within the Pre-Training team. Combines software engineering, ML and kernel design to remove performance bottlenecks.

JAX · PyTorch · XLA +4

• London
• Hybrid
• Senior
• Permanent

Hands-on research and engineering role on the Agent Code team, developing code-generating LLMs and autonomous agent systems for enterprise use. Involves training code models, building agent frameworks for inference and sampling, creating SFT trajectories, working on RL algorithms, and designing benchmarks, in collaboration with the pretraining team on frontier compute infrastructure.

PyTorch · TensorFlow

• United Kingdom
• Remote
• Mid-level
• Permanent

Join the research team at an AI audio company, training state-of-the-art models across modalities and improving them through post-training, data curation and architectural changes. You'll design rigorous benchmarks and evaluations to judge whether new model iterations are genuinely better, working autonomously in lean, high-velocity teams.

GitHub

• London
• On-site
• Senior
• Permanent

Builds and scales generative and predictive models of cellular behaviour, acting as the bridge between ML theory and implementation. Day to day involves optimising large neural networks, distributed multi-GPU/multi-node training, profiling GPU workloads, and productionising research code for multi-modal biological data.

PyTorch · CUDA · Triton +1