You will execute A/B tests, multivariate tests, and personalisation campaigns using platforms like Optimizely, VWO, and Adobe Target, working with analytics suites such as Google Analytics 4 and Adobe Analytics, plus CMS and CDP tools. The role drives daily execution of the Rapid Iteration & Optimisation framework, collaborating with Paid Media, CX, and Development teams to deliver performance-enhancing site updates.

Works with A/B testing tools (Optimizely, VWO, Adobe Target), analytics (GA4, Adobe Analytics) and CDPs (Segment, Tealium), plus basic HTML, CSS, JS. Executes daily high-velocity site optimisation and continuous improvement for global clients within WPP Enterprise Solutions, collaborating with Paid Media, CX, and Development teams.

International SOS★3.4 (566)· London

Python, scikit-learn, TensorFlow, PyTorch, XGBoost, LangChain/LangGraph, RAG, AWS Bedrock/SageMaker, Azure/GCP, vector stores, SQL, Kubernetes, and Git. You will build production-grade generative and agentic AI solutions—RAG pipelines, multi-step agents, and predictive models—for the company’s product platform.

Zaizi★4.0 (5)· London

Quantize and deploy open-source SLMs (Gemma 3, Llama 3) and vision-language models using LiteRT/TFLite, ONNX Runtime, and ExecuTorch. Design knowledge graph pipelines and on-device graph databases in Python, Rust, or C++, and build MLOps pipelines for air-gapped environments. The team delivers deterministic, explainable, human-in-the-loop AI decision-support tools for UK Defence intelligence users.

Lineten★2.6 (5)· London

Build cloud-native services using C#, .NET, Azure, and Microsoft Fabric with a Medallion architecture, while integrating AI/LLMs and automating infrastructure. You’ll own end-to-end development, data pipelines, and site reliability for a small startup team building internal and external data self-service solutions.

Arup★4.0 (352)· London

Python, Git/GitHub, pytest/unittest, CI/CD pipelines, AWS/GCP/Azure, Terraform/Docker, MATSim, Java, and PopulationSim. The Transport Modelling and City Modelling Lab teams build agent-based and activity-based models to solve urban transport challenges, using data and software engineering to plan future mobility systems.

Staff Machine Learning Engineer role at Sony Interactive Entertainment's Visual Computing Group, working with TensorFlow or PyTorch and Python for graphics and video processing. The team designs and deploys neural networks for game rendering and live streaming R&D. Requires 7+ years experience and publications at top-tier conferences.

Build production-grade generative and agentic AI using Python, scikit-learn, TensorFlow, PyTorch, XGBoost, LangChain/LangGraph, RAG, AWS Bedrock/SageMaker, Azure, GCP, vector stores, SQL, Kubernetes, and CI/CD for the company’s product platform.

You will work with Java microservices, Spring Boot, Python, SQL, Snowflake, dbt, Airflow, LangChain, Sagemaker, and MLflow. The data science team builds ML solutions for risk-based decisioning, pricing, and operational automation in insurtech/fintech. This is a hands-on leadership role—you will lead and develop the team while setting technical standards.

You’ll build cloud-native apps with C#/.NET and Azure, design Microsoft Fabric and Medallion data pipelines, handle ETL/ELT, SRE, and integrate LLMs via OpenAI/Azure OpenAI. Based at startup Lineten Ltd., you’ll join a small team enabling internal and external data self-service analytics.

Deloitte★3.9 (14k)· London

You will work with Python, NumPy, Pandas, scikit-learn, LangChain, Hugging Face, LlamaIndex, Azure Machine Learning, Foundry, Git, and Agile practices. The team is Deloitte’s Tax Digital Innovation AI & Data Studio, which researches, builds, and delivers analytics and AI-driven solutions for tax and legal clients.

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 the PyData stack (pandas, numpy, scikit-learn, XGBoost) and advanced ML methods like neural networks to build predictive models and proprietary metrics from high-frequency cricket tracking data. CricViz is a market leader in cricket data analysis, serving broadcasters, teams and gaming platforms.

You will work with Python, NumPy, SciPy, simulation modelling, optimisation under uncertainty, cloud deployment, and machine learning. The team builds an AI operating system for secure environments, taking complex scientific models from prototype to production across government and commercial programmes.

You will work with kdb+, q, Python or Java, APIs (REST, messaging), Linux, AWS/GCP, Docker/Podman, and CI/CD. You will develop, test and maintain kdb+ applications and supporting services for market data and analytics on a Client Engineering team for a FinTech leader. You will also mentor junior developers.

Use Python, scikit-learn, TensorFlow, PyTorch, and geospatial tools like QGIS and ArcGIS. As a Machine Learning Engineer at a technology consultancy, you will design, build, and deploy ML models

Senior Software Developer role in the Data team of a global energy trading house, working on data-consumption and publishing platforms using C#, .NET, ASP.NET, Angular, SQL Server, Oracle, Docker, Grafana, Kafka, Azure Service Bus, and Azure DevOps. Responsible for end-to-end delivery, mentoring, and architectural direction.

You will work with Python (pandas, NumPy, SciPy, matplotlib/seaborn), SQL, cloud data tools (ideally Azure), and large language models (LLMs) to build predictive models and identify alpha signals. You will join the data science team of a leading investment management company.

Wise★3.5 (42)· London

You will build scalable systems for business onboarding and verification using Java or JVM languages, Spring framework, JDBC, and relational/non-relational databases with CI/CD pipelines and distributed systems. This engineering team owns end-to-end projects to automate verification, create flexible onboarding journeys, and manage compliance risks for Wise’s money-moving platform across Europe and the UK.

Lead ML engineering using Python, PyTorch, TensorFlow, MLOps (CI/CD, experiment tracking, model registries) to build computer-vision and generative-AI systems. The role delivers live-sports insights—player performance, contextual statistics, injury risk—and integrates them into personalisation engines, with mentoring of Senior and Mid-Level Data Scientists.

PlayStation★3.8 (164)· London

This role involves working with TensorFlow or PyTorch, Python, and libraries for computer vision and graphics. As part of the Visual Computing Group (VCG), you will develop generative models to advance game rendering and live video streaming through neural networks and machine learning R&D.

Moody's★3.6 (589)· London

Lead validation of credit rating models and AI model risk management at Moody's, working with R, Python, MATLAB, SQL, and preferably C++. The MRG Quantitative Review team independently validates quantitative models and scorecards supporting credit ratings, with a focus on Generative and Agentic AI risks. Requires 10+ years in model development, validation, and risk analytics.

Chubb Insurance★3.7 (1.3k)· London

You will use Python, Hugging Face Transformers, LangChain, LlamaIndex, PyTorch, TensorFlow, Docker, and cloud platforms to build and deploy LLM, RAG, and conversational AI applications. As part of Chubb Insurance's Global Analytics team, you'll design production-ready AI solutions for business value.

You'll work with Java/JVM and Spring on the backend, React/JavaScript/TypeScript on the frontend, plus CI/CD, distributed systems, and relational/non-relational databases. You'll join Wise's Account Integrity team, building fraud-prevention and financial-crime detection systems to protect customer accounts. Wise is a global technology company for moving and managing money.

Tesco★3.5 (25k)· London

You will work with Java, Spring Boot, Kafka, Apache Flink, Apache Beam, NoSQL databases, Redis, and Apache Camel. The Tesco Personalisation team builds real-time, low-latency services for recommendations, offers, and experimentation across web, mobile, and in-store channels using streaming data and ML.