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.

Use Selenium, Playwright, Postman, JMeter, Jira, SQL, Git, Jenkins, and Azure DevOps to design and execute manual and automated tests. Validate merchant onboarding, payment processing, settlements, terminal management, and Lloyds Accept application flows across web, mobile (iOS/Android), and API systems.

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.

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

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.

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.

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.

Use Python, PyTorch, TensorFlow, scikit-learn, LLMs, NLP, and SQL to build and deploy ML models for trade surveillance. You'll enhance analytics that detect market manipulation by analyzing trader behavior and order execution. Collaborate

Imperial College London★4.2 (238)· London

Work with PyTorch, GPU kernel programming, Verilog, and NVIDIA's Holoscan Sensor Bridge to integrate FPGA-based AI-ISP accelerators with edge GPU platforms. The project targets low-latency, high-throughput sensor-to-GPU streaming for the "Sense-Decide" pipeline in edge automation.

You will work with Python, PyTorch, TensorFlow, and causal inference tools (DoWhy, EconML, CausalML) to build production AI pipelines, APIs, and microservices. You will design and deploy behavioural, sequential, and causal AI models into Digital Twins, agentic decision engines, and intelligent automation. No team size is specified.

HSBC★4.0 (16k)· London

Work with Python, Docker, Kubernetes, Flask/FastAPI, PyTorch/TensorFlow, SQL Server/Postgres, and CI/CD pipelines to build and deploy production-grade ML and GenAI services. You will join the Growth AI programme within HSBC’s Corporate & Institutional Banking Data & Analytics, developing AI applications to identify business opportunities and mitigate client attrition.

You’ll work with AppsFlyer, Google Ads API, Meta Ads API, GCP, Mixpanel, Databricks, Power BI, SQL, and optionally dbt and Python. This senior individual contributor role supports the Marketing function of Moneybox, an award-winning wealth management platform, focusing on marketing attribution, martech data infrastructure, and building data products.

You will work with Python, PyTorch, JAX, and TensorFlow to develop optimisation, control, and machine learning methods including reinforcement learning and physics-informed ML. The role is in UCL Computer Science's Multi-Sensory Devices Group, whose research covers wearable sensing, robotics, and biomedical devices.

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.

UCL★4.2 (299)· London

Work with Python, PyTorch, JAX, TensorFlow, reinforcement learning, physics-informed ML, differentiable programming, and model-based control. The role is in UCL’s Multi-Sensory Devices Group, researching wearable sensing, robotics, and embodied AI. Develop and evaluate novel methods for accurate trajectory control in complex physical systems.

Linux Recruit★3.0 (1)· London

Work with PyTorch, distributed training libraries, Slurm, Kubernetes, and AWS multi-node GPU clusters to architect and optimize large-scale model training pipelines. You will join a stealth AI startup at the intersection of robotics and embodied AI, building the infrastructure that powers next-generation AI breakthroughs.

Starling★3.5 (66)· London

Work with Swift (iOS) and Kotlin/Java (Android), native mobile security APIs and cryptography, and public cloud IaaS. You will design, build, and maintain security controls for the company’s Android and iOS mobile banking applications as part of the Cyber Security team.

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'll build backend services and microservices using Rust, Go, Python, and Java for X Money, xAI's global payment network targeting 600+ million users. The role involves designing high-scale, low-latency distributed systems for payments, fraud detection, and compliance while collaborating with iOS, Android, and web teams on full-stack features in a startup-like environment.

You will work with Python, PyTorch, PyTorch Lightning, TensorFlow, and experiment tracking tools to design and implement custom machine learning model components for Search, Retrieval, Ranking, or Content AI. You will join the Tripadvisor Experiences R&D Team, which is evolving the marketplace for travel experiences by powering how millions of users discover and plan itineraries. You will act as a technical lead and pod architect, and mentor mid-level and associate ML scientists.

Design, build, and deploy quantitative models using Python, R, and libraries (numpy, pandas, scikit, pytorch, tidyverse, caret, ggplot) plus NLP tools, SQL, AWS, Sagemaker, DataBricks, Snowflake, MLFlow, and Weights & Biases. The team creates systematic country risk analytics and ML/NLP pipelines for BMI’s market insights, supporting clients in risk assessment.