Snowflake★3.7 (31)· London

You'll work with Python, SQL, LangChain, Hugging Face, PyTorch, RAG, and modern data architectures (warehousing, lakes, lake house). As a Solution Engineer, you'll present Snowflake's AI Data Cloud, deliver demos and proof of concepts, and collaborate with sales and channel partners to drive adoption.

Snowflake★3.7 (31)· London

Work with Python, SQL, and modern AI/LLM ecosystems including RAG, LangChain, Hugging Face, and PyTorch, supporting data warehousing, data lakes, and lake house architectures. As a Solution Engineer at Snowflake, you drive sales cycles, deliver demos, and run enterprise Proofs of Concept for the AI Data Cloud.

JPMorgan★3.9 (20k)· London

You will work with Java, TypeScript, React, Scala, SQL and No-SQL databases on cloud-native platforms. As part of JPMorganChase’s Data Platforms team within the Chief Data & Analytics Office, you will engineer solutions in metadata, meta-modelling, distributed systems, domain-specific languages and interpreter design. You will provide technical guidance to the team.

Deliveroo★3.3 (1.5k)· London

Build production infrastructure for Generative AI at Deliveroo and DoorDash, leading design and architecture for real-time GPU serving, high-throughput batch inference, and fine-tuning (SFT/DPO/LoRA) of open-weight LLMs and VLMs. Own the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution, while mentoring engineers and setting technical direction for a small, high-leverage centralized GenAI platform team.

You will deploy ML models and engineering surrogates (e.g., deep learning on CAE/CFD/FEA data) to production environments, using Python, PyTorch, Pandas, fastAPI, Scipy, and Kubeflow. PhysicsX builds an AI-driven simulation software stack for engineering and manufacturing.

JPMorgan★3.9 (20k)· London

Senior Lead Software Engineer role on JPMorgan's Data Platforms team in the Chief Data & Analytics Office, working with Scala, Java, Cats Effect, and cloud-native technologies. The agile team builds secure, scalable technology products spanning metadata, meta-modelling, and distributed systems, with a focus on AI-assisted engineering and domain-specific languages.

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.

You will work with Python (Django/FastAPI), TypeScript, React, Astro, Svelte, PostgreSQL, AWS, Terraform, and frontier AI models via Amazon Bedrock. You'll own technical direction, mentor engineers, and build AI products for UK public services in health, education, planning, and Parliament. The role is in small, empowered, cross-functional teams.

You'll work with Python, PyTorch, SGLang, large language models, embeddings/reranking models, PEFT/LoRA, RAG, prompt engineering, Git, Docker, microservices, and cloud infrastructure. The early-stage company builds AI-powered products transforming specialised financial markets, combining machine learning with real-world trading workflows. You'll collaborate with a small engineering team, founders, brokers, and traders on commercially impactful systems.

The role uses SQL, R, and/or Python for advanced statistical modeling, machine learning, and psychometric analysis. It is part of the Talent Insights and Analytics function at EY, where you design and conduct organizational research to inform strategic talent decisions and workforce interventions.

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.

You will work with Python and large language models (LLMs) to build AI solutions and AI agents. The company develops cutting-edge AI products for clients, with a focus on innovation and creative freedom.

You'll build production-ready machine-learning systems in Python, applying deep learning and data-pipeline work to model human behaviour at scale for a London AI startup. The role turns unstructured human data into accurate, commercially viable population simulations, requiring high autonomy to ship fast, high-quality solutions.

Python, TypeScript, React and PostgreSQL power this Senior Full Stack Developer role. The work involves building and owning a flagship AI platform that handles large-scale data processing, entity resolution and production AI workflows within a small, experienced engineering team.

LSEG★3.7 (403)· London

Core AI Engineer role at LSEG building enterprise-grade AI products using Python, LLMs, GenAI, agent frameworks, RAG architectures, vector databases, APIs, microservices, and cloud-native development (Azure preferred). The AI & Automation team transforms LSEG operations through AI, automation, and intelligent engineering, delivering scalable AI solutions. Requires designing architectures, MLOps, CI/CD, and technical leadership across AI initiatives.

Expedia Group★3.8 (1.2k)· London

This Machine Learning Scientist II role develops and optimizes ranking models using machine learning and GenAI for travel lodging search and recommendations. The position involves Python, common ML frameworks, A/B testing, and deploying models into production. You will work on the Lodging Search Ranking AI team, collaborating with analytics, product, and engineering to improve traveler experiences.

You'll work with Python, TypeScript, LLM-powered systems, AI agents, and data pipelines. This London AI startup builds an evaluation layer and platform for strategic finance teams to automate complex modeling and analysis. As founding engineer/CTO, you'll own the entire technical stack and architecture as the first engineering hire.

You'll work with Python, FastAPI, TypeScript, React, and GCP, building services, data models, and APIs for a behavioral simulation platform that creates digital twins of populations. The team of 17 owns complex systems from design to deployment, orchestrating foundation models and data pipelines for major organizations.

Build features across Tab's Svelte/TypeScript front end and Python back end (FastAPI, Django), using PostgreSQL and AWS. The YC-backed travel fintech processes thousands of real-time payments per hour in 100+ currencies for 100,000+ tourism businesses.

Build and deploy production-grade agentic systems and RAG pipelines using Python/FastAPI and TypeScript/React. Lead the architectural development of an AI-driven agency model, replacing manual workflows with autonomous agents for the global property market.

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.

Build LLM-powered agentic systems with persistent memory and tool orchestration, integrating Google Ads, Meta, and CRMs. Iterate on RAG pipelines, retrieval strategies, and domain-specific fine-tuning in Python. As the second ML hire, you deploy production-grade autonomous agents automating marketing operations for a Series A startup.

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.