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.

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.

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.

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.

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.

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.

You will work with Python, LLM agents, inference/serving, reinforcement learning, multi-agent orchestration, search/retrieval pipelines, and modern AI coding tools. As a DeepMind Research Engineer, you bridge research and scalable systems, collaborating with scientists on machine forecasting to build 0-to-1 infrastructure, optimize LLM architectures and RL pipelines, and engineer evaluation platforms for autonomous reasoning under uncertainty.

This role develops generative AI applications using Java, Python, React or Angular, LangChain, LlamaIndex, Hugging Face, Docker, Kubernetes, and AWS, with tools like GitHub Copilot. You will build scalable, AI-powered solutions for Citi’s global finance operations.

You will use Python, KQL, SPL to build detection logic and automate response. You will protect Writer’s AI platform, training data, and model deployments, detecting threats like prompt injection and model extraction. You will coordinate incident response across teams and hunt for threats in GPU clusters and distributed training environments.

Work with Python, PyTorch, diffusion models and flow matching. Join an interdisciplinary team deploying, adapting and optimising generative models for real-world scientific and industrial applications at an AI research company developing models for scientific discovery.

Work with Nvidia GB200 72NVL CX8 and AMD MIxxx architectures, coding in C, C++, C#, Java, JavaScript, or Python to benchmark, profile, and debug GPU clusters. You will help build foundational models powering Microsoft’s personalized AI assistant, Copilot, spanning training and inference infrastructure, model architecture, and data curation.

Design and develop next-generation Vision Transformers and multimodal LLMs using PyTorch. Work on multimodal alignment, representation learning, and large-scale distributed training for image, video, audio, and text datasets. Join a research organization advancing multimodal AI and embodied intelligence.

Lead a hands-on founding engineering role building AI agents for data privacy compliance at Pyxos. Own the control plane, agent architecture, model-abstraction layer, telemetry, evaluation harnesses, and expert knowledge system using LLMs, multi-step agentic workflows, and CI/CD pipelines. You will work with a production system serving enterprise customers, hiring a deliberately small team as the most senior technical leader reporting to the CEO.

You will work with Python and JAX, contributing to the Gemini large-scale pretraining project at DeepMind. Responsibilities include improving training workflows, tooling, and codebase to support research and address issues in large-scale runs.

Work with Python, modern ML tooling, and LLM inference infrastructure to build and deploy specialized AI agents. You will own the end-to-end AI/ML stack for a research-led company that builds simulations and language models for social science and public policy problems.

Build production-grade generative AI systems using Python, LlamaIndex, LangChain, LangGraph for RAG pipelines and agentic workflows at an award-winning B2B consultancy. Emphasizes evaluation for correctness and reliability, with hands-on technical leadership and mentoring.

You will work with C++ (including memory management) and Python (Pandas, NumPy) to build high-performance infrastructure, data pipelines, and quantitative trading systems. The team develops and optimizes simulation and backtesting tools and handles high-volume market data feeds.