You will use Python, PyTorch, Hugging Face Transformers, and DeepSpeed to build semantic chunking, document enrichment, and LLM-based knowledge graph systems for the legal AI platform. Your work directly powers search, information extraction, and agentic reasoning in products used by millions of legal professionals. You will serve as the technical lead and mentor team members with varied ML/NLP abilities.

You'll work with 5G/6G, machine learning, large AI models, LLMs, and AIGC to develop a wireless network digital twin system for smart city logistics and last-mile applications, including performance analysis, radio propagation modelling, and network optimisation for EU/UK projects.

You will work with Java, C++, Python, generative AI, and large models to develop and deploy customized machine learning models at scale. The Prime Video Observability team delivers AI-powered log management and tracing solutions to help developers detect and diagnose issues.

Amazon★3.5 (56k)· London

Develop generative AI and large models using Java, C++, Python, and Unix/Linux for Prime Video’s observability team, which combines log management, tracing, and AI-powered analytics to detect and diagnose issues at unprecedented speed.

Design, develop, and evaluate machine learning models using Python, PyTorch, TensorFlow, statistical modelling, deep learning, and multimodal AI for the ASOS AI Demand Forecasting platform. This platform provides demand forecasts to power replenishment, AI-powered pricing, and supply chain optimisation. You will also mentor other scientists and provide technical leadership on complex modelling projects.

This role uses generative AI, large models, and neural deep learning methods with Python/Java/C++ and tools like TensorFlow or Spark. You’ll develop personalized recommendations and ML algorithms for the Prime Video Insights team, building high-scale features that reduce operational load for internal builders.

Amazon★3.5 (56k)· London

You'll use Java, C++, or Python and AI coding assistants to build and ship real-time machine learning models for ranking, valuing, and bidding ads on Amazon's Demand-Side Platform. The team aims to make ads more relevant for shoppers while balancing advertiser and Amazon objectives, working across Edinburgh and London within a larger UK/US group.

The role uses Python, ML, statistics, and LLMs to protect Prolific's platform from fraud by identifying expert participants. As the first data person on the Integrity team, you'll shape the role, collaborate across teams, apply risk modelling or classical ML, turn ambiguous problems into experiments and metrics, and iterate on prototypes to build production solutions.

You will work with Python, PyTorch, TensorFlow and distributed data processing to develop and deploy machine learning models. The AI Demand Forecasting team builds scalable forecasting capabilities supporting replenishment, pricing and supply chain optimisation. No team size is stated.

You will work with Python, classical ML, statistics, LLMs, and agentic techniques (RAG, LLM-as-judge, multi-agent workflows). The role designs and prototypes AI/ML methods to improve human data quality, judgement aggregation, and AI evaluation workflows for Prolific's human data platform.

You'll design and build production models for campaign optimisation using Scala services, SDKs, and data pipelines. The team builds Permutive's data collaboration platform, which uses edge technology and AI to process data securely for privacy-preserving advertising.

Amazon★3.5 (56k)· London

As an Applied Science Intern, you will work with machine learning, deep learning, generative AI, large language models, speech technology, robotics, computer vision, optimization, operations research, quantum computing, and formal methods, using Java, C++, or Python. You will design and develop end-to-end systems, working with Amazon scientists to create algorithms and models that are deployed into production.

Amazon★3.5 (56k)· London

You will work with Java, C++, Python, neural deep learning, machine learning, TensorFlow, PyTorch, Spark, and formal methods (model checking, interactive theorem proving, property-based testing). The Agentic Automated Reasoning Group builds next-generation software verification tools combining AI and cloud computing to analyze code—from Infrastructure as Code to assembly—for provable security, availability, and correctness across AWS services.

Amazon★3.5 (56k)· London

Develops personalised recommendations using Java, C++, Python, neural deep learning, and tools like TensorFlow, Spark, and MLLib. The Prime Video Insights team builds observability features that reduce customer-impact time, consuming petabytes of data daily for internal builders.

Amazon★3.5 (56k)· London

Build machine learning and optimization models for Amazon’s Middle Mile freight marketplace using Java, C++, Python, MxNet, TensorFlow, AWS, and causal inference. The team develops forecasting, dynamic pricing, and network optimization systems to power Amazon’s external freight transportation network.

Thomson Reuters★3.9 (4.3k)· London

You'll work with PyTorch, JAX, HuggingFace Transformers, vLLM, LLM APIs, Ray, Spark, and experiment tracking tools like Weights & Biases and MLflow on Thomson Reuters' Foundational Research division, which develops advanced algorithms and training techniques for Large Language Models including LLM training, post-training techniques for reasoning and workflows, data-centric ML, and evaluation. You'll design and implement scalable systems for training, fine-tuning, and evaluating LLMs while translating cutting-edge research into production implementations.