BlackRock★3.8 (632)· London

You will design and develop distributed Java applications using Spring, Spring Boot, REST APIs, streaming technologies like Kafka and Apache Flink, relational databases (MS SQL Server, PostgreSQL, Sybase ASE), and Microsoft Azure. You will join BlackRock Global Markets’ In-Business technology team, building reliable, scalable technology to support the firm’s investment processes and platform architecture.

You'll work with Python (pandas, OOP), SQL (PostgreSQL, Snowflake), CI/CD pipelines, Git, and GitHub. You'll develop and support in-house fund management tools that assist Fund Managers in managing funds for Legal & General's asset management division.

Atlassian★3.9 (61)· London

Leading a distributed EMEA team, you'll use Python, JavaScript, APIs, and microservices to build AI-powered solutions with generative AI, LLMs, and agent-based frameworks. The Forward Deployed Engineering team partners with top organizations to solve complex problems using Atlassian’s AI capabilities.

Working with Python, C++, and deep learning frameworks such as TensorFlow, PyTorch, or JAX, the role covers LLMs, deep learning optimization, reinforcement learning, and generative models. The research team applies AI to semiconductor design and optimization, alongside fundamental research supporting internal applications and the broader scientific community.

You’ll implement ML research ideas using Python or C++, alongside deep learning frameworks like TensorFlow, PyTorch or JAX. The team applies AI (including LLMs, reinforcement learning and generative models) to semiconductor design and fundamental research.

Build and enhance data-driven solutions using Scala, Python, Java, Hadoop, and Spark (Hive nice to have) in a financial services environment. Work with cross-functional teams on scalable, high-performance applications, participating in code reviews and performance optimization.

Use Python (pandas, NumPy, SciPy, matplotlib/seaborn) and SQL with cloud-based data tools and large language models. Build predictive and statistical models to identify alpha signals from alternative data for a leading investment manager with a global presence.

The Machine Learning Engineer will work with Python, R, C++, TensorFlow, PyTorch, and scikit-learn to build and deploy predictive models. The role supports materials research in solidification, casting, and alloy development, applying ML to process optimization and industrial decision-making. The engineer collaborates with materials scientists to translate research questions into ML solutions.

Lead the end-to-end development of AI solutions using Computer Vision, Machine Learning, Generative AI, and data science, with Python, PyTorch, TensorFlow, and MLOps practices (CI/CD, experiment tracking, model registries). The role delivers model-driven insights for player performance, injury risk, and personalisation in live sports scenarios, while providing technical leadership and mentoring Senior and Mid-Level Data Scientists.

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.

You will work with Python, FastAPI/Flask, PostgreSQL, MySQL, BigQuery, GCP and/or AWS, and IAC/CI/CD tools to build production-grade data pipelines and MLOps infrastructure. You’ll join Ki’s commercial performance insights squad to upgrade algorithm decision data to a versioned event stream and support a Monte Carlo simulation engine for an algorithmic insurance carrier. The role is in an agile, cross-functional squad.

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.

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.

You'll work with Python, PostgreSQL, MySQL, BigQuery, FastAPI, Flask, GCP/AWS, and CI/CD pipelines. You'll join the commercial performance insights squad to build data pipelines and event streams for monitoring algorithmic insurance underwriting, including Monte Carlo simulation.

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.

This role involves leading AI/ML delivery using Python, LLMs, and techniques like prompt engineering, RAG, and agentic AI, with experience in frameworks such as PyTorch or TensorFlow. The team, a 150-strong AI and Data Practice, delivers production-grade AI solutions for clients in government, healthcare, defence, and commercial sectors. The manager will coach and develop a multidisciplinary team.

You’ll work with Python, PostgreSQL, MySQL, BigQuery, FastAPI/Flask, and GCP/AWS to build production data pipelines and MLOps infrastructure for Ki’s algorithmic insurance platform. You’ll act as a data engineer in the commercial performance insights squad, designing systems that monitor algorithmic underwriting decisions in real time.

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.