You will work with OpenMM, OpenFE, molecular dynamics, MM/GBSA, free-energy perturbation (RBFE/ABFE), and GPU infrastructure to define and validate automated biophysics tools. The team builds a scalable platform for agentic drug discovery workflows, ensuring scientific correctness in molecular simulation.

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.

Bloomberg★3.8 (1.1k)· London

Define strategy for AI Platform Experiences & Integrations within Bloomberg’s CTO Office. Work with UX and engineering teams on generative AI, agent systems, and emerging interfaces (MCP, A2A, A2UI). Product teams interact with these platforms through agent-driven experiences, copilots, and workflow orchestration surfaces.

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.

Use Python, SQL, graph databases, Docker, Kubernetes, and LLMs to own Kharon’s risk-propagation engine that evaluates entities in a knowledge graph and surfaces risk signals. The role spans research, data engineering, and product to evolve a production system for global security analytics.

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.

You will own Relay’s Demand Forecasting models, working hands-on with Python, ML frameworks, and forecasting tools to predict parcel volume, weight, and size at outcode granularity out to D30. You set the technical direction for the Network squad’s forecasting engine, which drives sortation, middle-mile, last-mile, and expansion decisions. The centralised data team has ~30 people, with you acting as the senior IC anchor, mentoring a Senior Data Scientist and an Analyst.

You'll work with TypeScript, React, SQL, Amplitude, GrowthBook, PostHog, LaunchDarkly or Statsig, and Customer.io or Braze. The company is building a senior AI employee for small-business owners that drafts client work from existing tools. You own the activation funnel, experiment framework and retention loops as the first growth engineer on a small senior team.

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.

Lead UI Developer role using TypeScript, React, RxJS, WebSockets/SSE, GraphQL, Protobuf, Flatbuffers, Jest/Vitest, React Testing Library, Playwright, and AG Grid. You will own the front-end architecture for a real-time Single Dealer Platform used by Sales, Trading, and institutional clients globally, building low-latency trading applications for market data, pricing, RFQ, and trade capture.

TypeScript, React, Node.js, Bun, Kubernetes, and AWS. The team builds web apps, back-office systems, and tooling for financial product comparison across money.co.uk, Uswitch, and Confused. As a Staff Software Engineer, you set technical direction for Financial Services.

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.

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 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

DeepL★3.8 (4)· London

You'll work with C# / .NET, RESTful APIs, distributed architectures, and optionally React/TypeScript, plus MCP, CLIs, OAuth, and AI coding tools. You join the API Enterprise team, which builds enterprise adoption features (usage insights, cost controls, RBAC, compliance, data residency) for DeepL's Language AI API platform.

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.

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.

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.

A Senior AI Engineer at Stakemate will use agent frameworks (LangGraph, LlamaIndex, Semantic Kernel, ADK), RAG, Python, cloud services (Azure/GCP/AWS), and SQL to move AI from experiments to production. They will shape the platform and architecture for a live betting product at a fast-growing social sports betting startup.

Microsoft★4.2 (9k)· London

Work with AI and cloud platforms such as Azure ML and OpenAI, applying practices including prompt engineering, retrieval-augmented approaches, model tuning, and CI/CD tooling. As a member of the Frontier Company Engineering team, you will embed within customer teams to build production-ready systems and services that solve complex organizational challenges.

You will work with agent frameworks (LangGraph, LlamaIndex, Semantic Kernel, ADK), RAG pipelines, vector stores, Python, SQL, and a major cloud (Azure AI Foundry, GCP, or AWS). You will take AI agents from proof-of-concept to production within Stakemate’s live sports betting product, setting the governance and standards for the company’s AI estate. Reporting to the CTO, you will shape the platform and architecture as the company scales.

Python, PyTorch, JAX, LLMs, vLLM, vector databases and modern agent frameworks will be used. The stealth AI startup is building a pre-launch, next-generation AI assistant for everyday tasks, conversations and workflows — a 0-to-1 opportunity on a small engineering team.

Java back-end developer using microservices, REST APIs, AWS (Lambda, EKS, RDS, S3, MSK), CI/CD, and AI tools (GitHub Copilot, LLMs). Builds next-gen trading and cash management systems for the Short-Term, Cash, and Collateral desks, modernizing platforms and migrating to AWS.