Work with Python, SQL, JavaScript/TypeScript, NumPy, Pandas/Polars, visualization libraries, and deep learning frameworks (PyTorch, JAX, or TensorFlow) to design and deploy full-stack data-driven applications and machine learning solutions. You will be embedded with Arsenal's Men’s First Team Analysis department, building tools that directly support coaching and analysis workflows.

You will work with LLMs, chain-of-thought prompting, RAG architectures, multi-agent tool use, Python, and FastAPI to build cognitive systems. The Science team designs memory and decision-making frameworks to create synthetic populations for behavioral prediction.

Build and ship AI features using Python, PyTorch/JAX, LLMs (OpenAI-style APIs, LLaMA, Qwen), vLLM, and vector DBs. You'll work on a proactive smart assistant that handles long-running workflows, persistent context, multi-step reasoning, and external tool interaction for everyday users.

You'll work with Kubernetes, Terraform, Python, AWS or GCP, and CI/CD tools (GitHub Actions, GitLab CI, Argo CD), plus observability stacks (Datadog, Prometheus, Grafana). You'll bring SRE discipline to an AI platform hosting model serving, agent orchestration, and batch inference.

Optimize ML inference for edge accelerators and GPUs using TensorRT, CUDA, Qualcomm QNN, Triton, MLIR, and ONNX. The team deploys large transformer-based models on low-cost, low-power edge devices for Wayve's first driving product. You'll work across compilers, runtimes, and kernels with Python and C++.

You will develop and deploy LLM-powered applications using PyTorch, SGLang, and PEFT/LoRA, building and fine-tuning embedding and reranking models. You will work on an early-stage platform that combines machine learning with real-world trading workflows to transform specialised financial markets. You will join a small engineering team with direct collaboration alongside founders and traders.

Build REST and GraphQL APIs and MCP servers in Python (FastAPI, Flask) to expose ML models from Databricks to internal apps, CRM platforms, and AI agents at RBC. Work within the Data Science team to architect the serving layer, handling contract design, authentication, and documentation.

You'll build scalable AI data pipelines, cloud-native backend systems, and microservices using Python, FastAPI/Flask/Django, SQL, AWS/Azure, Docker, and Kubernetes. The company specialises in transforming AI research into secure, scalable production systems. You will also mentor junior engineers and contribute to architecture decisions.

Work with Python, PyTorch, JAX, CUDA, Docker, Kubernetes, Slurm, Ray, and Linux on GPU computing. The Molecular Intelligence team builds ML and physics-based infrastructure for drug discovery, including biophysics, chemistry, cofolding models, simulation workflows, and agent-usable scientific pipelines.

Senior Research Engineer at Decagon will work with Python, modern ML tooling, and LLM post-training and deployment to build conversational AI models powering the company's enterprise support agents. You'll own multi-quarter initiatives improving model reliability, capability, and efficiency through research in instruction following, retrieval, memory, and task completion, taking projects from prototype to production. The role requires 5+ years in AI/ML engineering with proven production LLM deployment experience.

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.

Work with BigQuery, Airflow, BigTable, DynamoDB, Python, Scala, Spark, TensorFlow/PyTorch, Docker, Kubernetes, and LLMs building end-to-end ML pipelines. You'll own ML-powered features for the Recommendations Experience team, connecting 200M+ users with personalized music.

You will work with Python, PyTorch or JAX, Docker, CI/CD, cloud platforms, and MLOps for model serving. The role builds and deploys scalable infrastructure for training, inference, and evaluation on molecular and protein datasets at an AI-driven protein design company.

You'll work with Python, LLMs for post-training (RL, fine-tuning, distillation), distributed training, and inference optimisation. This YC-backed AI startup builds frontier systems modelling complex large-scale real-world behaviour. As a Research Engineer, you'll own the path from research to production, working directly with the CEO and Head of Research.

C++ developers will work on ultra-low-latency exchange protocol encoders and automated trading strategies, using C++ and optionally Python, for a data-driven prop fund’s global automated trading systems. The role involves collaborating with researchers and ops in a small, fast-paced team operating in a real-time environment.

You will work with Python, LLMs, agentic workflows, retrieval-augmented generation (RAG), and internal AI platforms. The Tactical AI Automations team deploys and supports AI tools across PIMCO's Portfolio Management and Analytics groups, including operational support for Lane Assist and POET (portfolio optimization tools).

Lead 6-7 engineers on core ML infrastructure for a systematic quant fund: distributed model training, LLM hosting/fine-tuning, and scalable deployment. Use Python with working knowledge of C++/Java. Own technical direction for real-world, high-stakes systems.

Azure ML, AKS, Dagster, CI/CD, Terraform, Prometheus, Grafana, Python, Kubernetes, and vector databases. You will own the infrastructure layer for deploying, monitoring, and maintaining ML models and NLP systems serving local authority clients in health and social care.

The intern will design and build Python-based tools and mathematical models for real-world property decisions. Bowleys Oversight combines data, mathematical modeling, and AI to create residential property decision intelligence for landlords and investors. The role involves working directly with the founder to build the company’s analytical and technical foundations.

You'll work with Python, NLP/LLMs, SQL/NoSQL databases, and cloud infrastructure, designing retrieval, knowledge-base, and reasoning models. You own projects end-to-end at Catapult, which builds personalised AI that understands a user's full work context across email, calendar, Slack, and other tools. No team size is given for this founding role.

Waymo★3.0 (61)· London

You will use Python, deep learning frameworks (JAX, TensorFlow/PyTorch), and C++ (preferred) to develop data-selection algorithms and scaling laws for foundation model training. The AI Foundations team builds machine learning solutions for autonomous driving, focusing on reinforcement learning, generative modeling, and robust evaluation to improve the Waymo Driver. You will report to a Senior Staff Software Engineer.

You'll work with Python, deep learning, and large-scale machine learning models, building training and evaluation pipelines for vision-language-action and world models. You'll train models that interact with the physical world through real robotic hardware at a UK-based AI robotics startup.

EXL Service★3.7 (2k)· London

You will work with Python, FastAPI, LLM frameworks (prompt engineering, RAG, agentic systems), and cloud AI platforms (Azure OpenAI/AWS Bedrock/Claude) on Kubernetes. As a Sr. AI Architect & Client Partner at EXL, a data and AI services company, you will design and deliver production-grade Generative and Agentic AI systems for enterprise clients, converting ambiguous mandates into secure, scalable solutions while shaping deals and reusable IP.

Google★4.3 (6.2k)· London

Manage engineers building Google’s Learning Platforms within the Global Business Organization, which powers Google’s ecosystem of products and services for customers and partners. Work with back-end languages (Java, Python, Golang, C++) and front-end (JavaScript, TypeScript, HTML, CSS), plus ML model fine-tuning and evaluation.

Work with Python, PyTorch (or JAX/TensorFlow), RL fine-tuning frameworks like TRL or verl, and distributed multi-GPU training. The Reinforcement Learning Team advances RL, Bayesian optimisation, AI agents, LLMs, and vision-language models, applying them to AI for science, chemistry, physics, and robotics while publishing at top venues.