You will use PyTorch, JAX, and scikit-learn to build ML models in low-data regimes for drug discovery and molecular optimization. You will develop active learning, meta-learning, and uncertainty-aware approaches, designing data acquisition strategies that guide experiments, simulations, and compound selection.

The role involves training next-generation cofolding models using PyTorch, JAX, Boltz, AlphaFold-style methods, equivariant GNNs, contrastive learning, self-supervised representation learning, and DEL datasets. The scientist collaborates with ML researchers, chemists, biophysicists, and data engineers to improve protein-ligand binding prediction and AI-driven drug discovery. Lila Sciences builds an AI-powered operating system for autonomous scientific discovery across medicine, materials, and energy.

This role involves working with Python, PyTorch, JAX, CUDA, Linux, Docker, Kubernetes, Slurm, Ray, Flyte, and Argo. The team builds ML and physics-based infrastructure for drug discovery, including simulation workflows and agent-usable scientific pipelines. Engineers improve code quality, GPU efficiency, and distributed execution while making research tools scalable and maintainable.

Work with TensorFlow or PyTorch, Python, and related computer vision/graphics libraries on neural network architectures (convolutional, recurrent, transformer, diffusion-based). The Visual Computing Group (VCG) researches and develops generative models for game rendering and live video streaming to advance state-of-the-art runtime efficiency, visual quality, and latency.

Lead a team of Data Scientists and ML Engineers building AI solutions for financial markets using LLMs, Generative AI, Deep Learning, Transformers, Agentic AI, Python, PyTorch, TensorFlow, Hugging Face, LangChain, Semantic Kernel, Azure AI Foundry, AWS AI Services, and MLOps/LLMOps practices.

Lead a disputes team at Checkout.com, working with a .NET/C# back end, React/TypeScript front end, and Python for machine learning, alongside REST APIs, event-driven microservices, and SQL. The team builds tools and models to help merchants manage and win chargeback disputes at scale. You will oversee engineers across disciplines and seniority levels, driving predictable delivery and operational efficiency.

Work with Python, LLMs, RAG pipelines, vector databases, Hugging Face Transformers, LangChain, PyTorch, and Docker to build production AI systems. Design, develop, and deploy AI-powered solutions such as conversational AI, document intelligence, and backend services for the Global Analytics team.

Work with Java/Spring, React, JavaScript/TypeScript, CI/CD pipelines, and distributed systems. You'll join the Account Integrity team building a globally scalable fraud prevention engine to protect customer accounts and prevent financial crime using machine learning and real-time monitoring.

Design, build, and operate a Kubernetes-based MLOps platform using Helm, Terraform, Kubeflow, KServe, vLLM, and JupyterHub. The platform supports the full model lifecycle—experimentation, deployment, and scalable inference—for AI, ML, and LLM workloads in production.

You'll build and operate a Kubernetes-based AI/ML platform using Helm, Terraform, Kubeflow, KServe, vLLM, and JupyterHub. This role focuses on creating infrastructure, tooling, and guardrails so MLOps and data science teams can reliably deploy and serve models at scale.

You will work with backend development, databases, APIs, cloud services and production machine learning systems. This is for a growing technology business. No specific project or team size is mentioned.

Monzo★3.5 (108)· London

You will build and deploy deep learning, graph-based, and sequence-based ML models using Python and SQL, with exposure to Go. You will design human-in-the-loop systems and autonomous agents to route, prioritise, and automate customer-support issues for millions of customers across thousands of support staff at Monzo. You will be one of four ML engineers embedded in product squads.

Monzo★3.5 (108)· London

You will work with LLMs, autonomous agents, RAG, sequence-based architectures, Python, SQL, and Go, developing and deploying machine learning models in production. You’ll lead the design of a human-in-the-loop system that predicts, identifies, and routes customer problems to scale support for Monzo’s banking products across multiple geographies.

You will work with Python, AI coding agents (e.g., Claude Code, Codex), and potentially AWS, Kubernetes, and Temporal. Unitary builds Virtual Agents — AI-powered software workers that automate complex workflows end-to-end in customer tools, focusing on commercial insurance, marketplaces, and primary healthcare.

Hudson River Trading★4.5 (2)· London

You will work with Python, Pandas, NumPy, R, and MATLAB to apply statistical analysis and machine learning to vast quantities of market data. The team builds and maintains predictive trading models for the company's high-performance trading infrastructure. You will research, develop, and test order execution methods to increase trading efficiency.

Hudson River Trading★4.5 (2)· London

You will use Python, C++, Pandas, NumPy, R, or MATLAB to build and maintain predictive trading models. As an algorithm developer, you research and test novel order execution and model training methods, applying

You will use TensorFlow or PyTorch, Python, and CV/graphics libraries, plus 3D engines like Unreal or Unity, to develop generative models for graphics and video. As part of the Visual Computing Group, you'll design neural networks and ML for PlayStation's game rendering and live

Quantitative Researchers at Jane Street build models, strategies, and systems for pricing and trading financial instruments, working with Python, petabytes of data, and a large GPU cluster. They apply statistical and ML techniques, from linear models to deep learning.

Checkout.com★3.8 (15)· London

Senior Engineering Manager leading the Disputes team, which builds tools, services, and machine learning models to help merchants manage and win chargeback disputes at scale. The team owns a full stack centered on .NET / C#, React and TypeScript front ends, Python-based ML, and event-driven microservice architectures.

Backend engineers use Python, Django, Django-REST-framework, GraphQL (Graphene), Celery, Postgres, and AWS (Packer, Consul, Terraform). They build and maintain the AI-driven energy management platform that powers utilities and optimises renewable generation. The global engineering team is around 1,400 people.

You’ll train ML models, work on ML libraries and workflows, and use a GPU cluster with H100/H200/B200s. Projects are real-world machine learning applications for Jane Street’s global trading business, testing ideas against noisy financial data. No team size is stated.

Native Kotlin and Jetpack Compose development on a consumer-facing Android app for in-store grocery trips, running on a known device inside live stores. Integrates on-device edge AI (ML Kit, TensorFlow Lite) and uses AI coding tools like Cursor or Claude Code. Reports to the Head of Android.

You'll work with PyTorch, TensorFlow, XGBoost, GPUs, CI/CD and containerisation on the Search & Recommendations team, building production-grade ML systems for personalised recommendations, product ranking and AI-driven styling at ASOS, covering the full lifecycle from experimentation to deployment.

Use deep learning frameworks, distributed computing, GPU-based training, and MLOps to build and deploy batch and real-time ML models. The Search & Recommendations team develops personalised recommendations, product ranking, and AI styling experiences for ASOS's online fashion platform, helping customers discover products.

Senior Machine Learning Engineer at ASOS, building production ML systems for personalized outfit recommendations and style discovery. You'll work with deep learning frameworks like PyTorch/TensorFlow, distributed GPU infrastructure, and MLOps practices including CI/CD, containerization, and observability. Collaborate with ML Scientists on batch and real-time models serving millions of customers.