Amazon★3.5 (56k)· London

You will work with Java, C++, or Python, and use reinforcement learning, recommendation systems, causal inference, and generative AI. The Prime Video Commerce Insights team personalizes the commerce journey for the Prime Video streaming service, replacing business rules with ML-driven decisions. This is a delivery-focused role in a multidisciplinary group of engineers and scientists.

Amazon★3.5 (56k)· London

This role uses GenAI/LLMs, NLP, computer vision, and multi-modal models to build low-latency production systems (Python; Java/C++/Scala) for brand safety, MFA detection, and fraud prevention. You will define the science vision for the Trusted Supply organization, which safeguards advertiser trust and ensures ad quality across all Amazon Ads surfaces. You will lead and mentor a team of 5+ scientists.

Amazon★3.5 (56k)· London

Work with LLMs, multimodal evaluation, ML models, Java/C++/Python, and Unix/Linux to build real-time content classification systems for Amazon's Ads Brand Safety team, protecting advertisers from unsafe and policy-violating content across web, mobile, CTV, and audio inventory.

The Lead Applied Scientist will work with large language models, agentic AI, Python, pandas, NumPy, scikit-learn, Databricks, MLflow, and Spark. The role is the senior scientific authority on AXIS Capital’s Data Science and AI Delivery team, building AI/ML solutions that turn data into information for specialty insurance and reinsurance decisions.

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.

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.

Boston Consulting Group★4.2 (539)· London

You'll develop testing and evaluation frameworks for generative AI and AI products using Python, PyTorch, and TensorFlow, ensuring they meet responsible AI standards for safety, security, and equity. The Responsible AI team at BCG X—a 3,000-person tech organization within the consulting firm—supports product teams across industries in building trustworthy AI systems throughout the software development lifecycle. The role requires 3-5+ years in responsible AI, 3+ years with generative AI models, and program/project management experience.

You'll lead a 6-person team of applied scientists building vision-language models for document data extraction and efficient mobile models (<5MB) using PyTorch, with ~50% hands-on coding time. The work spans vision-language modeling, document understanding, distillation, quantization, and few-shot learning to power Entrust's identity verification platform that detects fraud and deepfakes at global scale. You'll define roadmap with product/engineering, contribute code, and publish research while managing team development in a product-driven ML environment.