Machine Learning Jobs

CureAge Therapeutics · London

Lead the computationally guided design of a lipid nanoparticle delivery platform, applying chemical and biophysical principles to optimise LNP composition and formulation. Use AI-enabled chemistry, cheminformatics, predictive models and molecular modelling to prioritise formulation components, working closely with the experimental team and founding team in a small start-up.

Hadean · London

Lead a new AI-powered defence product team, line-managing AI/ML, simulation, infrastructure and web engineers while staying hands-on in TypeScript, React and Node. You own delivery end-to-end, hiring and people development, partnering with Product and technical leads, with hybrid working one day a week in Shoreditch.

Humanoid · London

Develops learning-based locomotion and whole-body control policies for humanoid robots, using reinforcement and imitation learning, generative models and motion primitives. Builds scalable simulation and training pipelines in Isaac Lab and MuJoCo, and deploys, debugs and fine-tunes policies on physical robots, tackling sim-to-real transfer and real-time inference latency.

PhysicsX · San Francisco, United States · US$150,000 – US$190,000 pa

Delivery-focused role embedding physics-aware AI models into customer tools, building reliable ML data pipelines and working with 3D point-cloud and mesh data. Spans data science and software engineering, translating R&D into reusable libraries and products, with regular on-site customer travel.

PhysicsX · New York, United States · US$150,000 – US$190,000 pa

Builds and deploys machine learning systems for industrial physics challenges, designing reliable data pipelines and working with 3D point-cloud and mesh data. Partners directly with customers on-site to embed AI models into practical tools, translating R&D outputs into reusable libraries and products. Requires Python, cloud, and MLOps experience with frequent travel.

University of Southampton · Southampton · £41,064 – £45,064 pa

Embed physics-informed generative AI tools into a marine vessel concept, generation and evaluation platform as part of a Knowledge Transfer Partnership between Compute Maritime Ltd and the University of Southampton. Develop and validate fast models for ship resistance, propulsion and energy efficiency using CFD and benchmark data, and integrate multidisciplinary optimisation methods into the NeuralShipper platform.

MFK Recruitment · Brentford · £75,000 – £100,000 pa

Develop an engineering-grade spiking neural network engine for machine vision applications in defence and security contexts. Translate advanced research into robust production software using C++ and machine vision techniques. Collaborate with researchers and engineers on SNNs, CNNs, and Transformers, with integration into operational systems.

Sanderson · London · £550 – £700 pd

Builds, deploys and maintains machine learning models and end-to-end ML pipelines in a modern cloud environment, writing production-quality Python and SQL. Works with data scientists, analysts and engineers to take AI products from proof of concept to production, including NLP and large language model work, plus monitoring and documentation.

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 extra compute, building auto-labelling pipelines that feed a data flywheel.

Isomorphic Labs · Lausanne, Switzerland

Architect and secure an AI-first drug discovery platform, reporting to the CISO. You will build AI/ML threat models, protect model artifacts, deploy LLM and agentic guardrails, and lead AI-specific incident response across distributed training and inference infrastructure.

University of Essex · Ipswich · £40,000 – £45,000 pa

A KTP Associate role embedded at Distag QCS in Ipswich, working with a large, messy ERP dataset to build machine learning models and data-driven tools. The post holder leads thinking on data science in a company with no in-house capability, balancing hands-on Python development with change management and communicating insights to non-technical colleagues.

Relation Therapeutics · London

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.

MFK Recruitment · Brentford · £70,000 – £95,000 pa

Design and deploy production-grade computer vision systems using deep learning techniques such as CNNs and Vision Transformers. Translate AI research into robust, real-world applications for defence and security environments. Work across the full development lifecycle, from research to deployment on cloud and edge platforms.

Isomorphic Labs · London

Research Engineers build and optimise state-of-the-art AI models for drug design, working across the full ML lifecycle from experimentation and evaluation to production deployment. The role involves developing robust codebases, data pipelines and training infrastructure, with a focus on architectures such as Transformers, GNNs and diffusion models, in close collaboration with scientists and engineers.

Maxwell Bond · London · £150,000 – £175,000 pa

Hands-on role building LLM agent and retrieval systems on a small UK core team, reporting to the Head of Engineering. Work spans LangGraph/LangChain workflows, RAG pipelines with Qdrant and GraphRAG on Neo4j, model integration across Anthropic, OpenAI and Bedrock, plus evaluation harnesses and observability. Token cost, latency and auditability are treated as engineering constraints throughout.

NVIDIA · Cambridge

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.

NVIDIA · United Kingdom

Develop algorithms and optimizations for NVIDIA's LPX inference and compiler stack, working at the intersection of large-scale systems, compilers, and deep learning. The role involves building high-performance runtime and compiler components, mapping inference workloads onto future hardware, and collaborating with hardware architects on codesign. Distinct focus on LLVM/MLIR, graph transformations, and spatial accelerators.