Work with Snowflake, dbt, Python, and SQL to transform and model data, build data workflows, and automate processes. You’ll join the Python Infrastructure team at Lendable, a fintech building credit and savings products.

You'll work with Python, SQL, Snowflake, and dbt to transform and model data, build data workflows, and automate processes. The role sits on the Python Infrastructure team at Lendable, a fintech company rebuilding consumer finance products like loans, credit cards, and car finance.

JPMorgan★3.9 (20k)· London

Work with KDB, Q, AWS, and optionally Terraform, Kubernetes, Python, C, C++, or Java. Develop and maintain applications storing large Options data sets for real-time and historical access within JPMorgan’s Equities Trading Data & Analytics team, migrating to AWS and re-architecting systems.

You'll work with Python, SQL, Snowflake, and dbt to transform and model data, build data workflows, and automate processes. This role sits on the Python Infrastructure team at Lendable, a fintech building technology to help people get credit and save money.

Wise★3.5 (42)· London

You will work with Java, Spring Boot, AWS, Apache Kafka, and potentially Trino. The Pricing Core team builds and maintains the main pricing platform that enables Wise to price its products, processing over 100 million requests daily across critical paths.

You will use deep learning frameworks, distributed computing technologies, GPU-based training environments, and MLOps practices to build and deploy batch and real-time recommendation, ranking, and personalisation models. This role is within ASOS's Search & Recommendations team, which builds the machine learning systems that help millions of customers discover products daily.

You will work with PyTorch, TensorFlow, deep learning models, recommendation and retrieval systems, and MLOps practices on cloud infrastructure. Joining ASOS’s Outfits Discovery team, you will build and operate large-scale production ML systems that power personalised outfit recommendations and AI-driven fashion discovery for millions of customers.

Develop novel ML architectures including GNNs and foundation models using PyTorch or JAX. You will drive the core discovery engine of an autonomous laboratory, bridging simulation and real-world physical experiments for materials innovation.

Generative AI, autonomous agents, and real-time rebalancing systems optimize paid acquisition across TikTok, Meta, and Snap. You will build a systematic marketing engine for EQUALS, a London-based music social network with 1 million monthly active users.

Post-train, fine-tune, and distil frontier language models using RL, distillation, and reward design in Python. Build production-grade simulation systems that model thousands of individuals in real-time for a Societal World Model at a YC-backed startup.

You’ll work with Python, FastAPI, TypeScript, React, and GCP. Electric Twin builds a behavioral simulation platform that creates “digital twins” of real populations, helping organizations test decisions with 95% accuracy.

You'll work with Python (FastAPI/Django), Svelte/TypeScript, and AWS to build features across a payments and discovery platform. Tab is a YC-backed travel fintech enabling 100,000+ global tourism businesses to accept payments in 100+ currencies.

Work on high-dimensional statistical inference and theory, conducting complex simulation and real data analysis with advanced programming skills. The NeST project is a collaborative EPSRC Programme Grant on network stochastic processes and time series, led by a team of nine investigators across six UK universities.

Fine-tune and distill small language models (Llama, Mistral) using quantization (GGUF, AWQ), vLLM, TensorRT-LLM, FlashAttention, and frameworks like Apple MLX or llama.cpp. The founding engineer will own the model layer, shipping production AI for a pre-seed startup building privacy solutions for tier-1 banks.

You will work with Python, PyTorch or JAX, Docker, CI/CD, and cloud platforms to deploy ML models for protein design. The team builds scalable infrastructure for training, inference, and evaluation across molecular and protein datasets, taking research prototypes into production.

You will build the AI platform for a London fintech that automates insurance claims, using LLMs, NLP, and ML for document ingestion, fraud detection, and coverage decisions. You will develop and deploy production-scale systems alongside founders and insurance domain experts.

You will work with C#, Python, TypeScript, JavaScript, React Native, AWS or Azure, and various databases (relational, graph, document, key-value) plus SQL. The role supports critical projects aligned with Net Zero ambitions.

You will work with Java, Spring Boot, microservices, REST APIs, AWS, DevOps and CI/CD pipelines. You’ll lead client projects for a global technology consultancy, mainly in financial services, focusing on migrating legacy systems to AWS and introducing cloud deployments. As Principal Java Developer, you will lead projects, but team size isn’t specified.

Build UI components using Vue.js, React, or Angular, plus JavaScript, HTML/CSS, and Figma. You'll join TransPerfect TechOps, which develops workflow improvements, core services for the GlobalLink platform, and scalable client interfaces. Work within an Agile (SCRUM/Kanban) team.

Global Payments★3.3 (3k)· London

The role involves managing a Bug Bounty Program using CVSS for vulnerability assessment, and working with tools like ServiceNow or JIRA. You will coordinate remediation with whitehat researchers and stakeholders within Global Payments’ Security Operations, supporting the company’s payment technology and software solutions.

NatWest Group★3.7 (1.2k)· London

Leading a team building cloud-hosted, event-driven microservices at NatWest Boxed, you will manage software and quality engineers across empowered squads while working with AWS, Google Cloud Platform, Java, Python, JavaScript, ReactJS, AngularJS, Kafka, Hadoop, PostgreSQL, Snowflake, and GitLab.

McKinsey & Company★4.2 (952)· London

You will work with deep learning, foundation models, Bayesian modelling, causal inference, LLMs, and computational chemistry/protein engineering. As part of McKinsey’s global scientific AI team, you help transform clients’ R&D in life sciences, chemicals, energy, and other scientific industries.

Imperial College London★4.2 (238)· London

Postdoctoral Research Associate role using R, Python, Google Earth Engine, drone-based multispectral and LiDAR data, satellite Earth Observation products, and machine-learning methods. The ADAPTA project integrates tropical forest field ecology, plant functional traits, and biodiversity data with remote sensing to quantify ecosystem resilience. Position involves leading international fieldwork campaigns and coordinating with partners across Europe and the tropics.

Imperial College London★4.2 (238)· London

You will develop machine-learning methods for protein design, using Python, deep-learning frameworks, and generative approaches such as diffusion and flow-based models. The work contributes to the ProteinCAD tool within the CADMUS consortium, which aims to create designed proteins as programmable scaffolds for organizing inorganic particles to manufacture advanced magnetic materials.

Morningstar★3.9 (185)· London

Work with Python, R, MATLAB, SQL, statistical and machine learning techniques, and AI. Build thematic and smart beta indexes for Morningstar’s Indexes New Product Development team, covering the full lifecycle from ideation to launch.