Use Python, Azure, Docker, Kubernetes, Apache Airflow, and GitHub Actions to build scalable ETL pipelines, financial data pipelines, and cloud-native infrastructure. This front-office role is at a systematic equity hedge fund modernising its quantitative research platform, working directly with portfolio managers and researchers. Experience with S&P Xpressfeed, Snowflake, Bloomberg, MSCI Barra, Databricks, and LLMs is advantageous.

Use Python, Azure, Docker, Kubernetes, Apache Airflow, and GitHub Actions to modernize a systematic equity hedge fund’s quantitative research and engineering platform. You will build scalable ETL pipelines, cloud-native infrastructure, and research-ready datasets, working directly with portfolio managers and quantitative researchers.

You will use Python, SQL, and relational databases to design, implement, and support fixed income pricing tools. The team develops and maintains scalable production software for daily pricing and supports research into new pricing algorithms using traditional ML and LLMs.

You will work with Python, SQL, LLMs, LRMs, and multi-agent architectures to build ML/NLP-powered solutions for alpha generation and financial workflows. The team creates alpha-generating datasets and develops intelligent agents and workflow solutions for quantitative investment use cases at RavenPack.

Hudson River Trading★4.5 (2)· London

This role develops and improves large-scale model training for HRT's “foundation models for markets,” covering kernel development (CUDA/Triton/Pallas/CuTe), parallelism, and networking. You will work on PyTorch, JAX, and XLA, co-designing models with researchers to make predictions about future market states.

Hudson River Trading★4.5 (2)· London

You will work with CUDA/Triton/Pallas/CuTe, PyTorch/JAX/XLA, CUDA Graphs, FPGAs, and ASICs on large-scale model inference. The HAIL team builds and deploys foundation models for markets, ingesting market and alternative data to predict future market states.

Jupiter Asset Management★4.3 (4)· London

Work with Python (pandas, NumPy, SciPy, matplotlib/seaborn), SQL, Azure cloud tools, large language models (LLMs), and Power BI. As a Data Scientist at this investment management firm, you build predictive models, research alternative data sources for alpha signals, and deliver data-driven solutions directly with investment professionals.

You will work with Python, TypeScript, AWS (Cognito, Lambda, Fargate, API Gateway, S3, IAM), Terraform, PostgreSQL, and AI tools (Claude, GitHub Copilot, Cursor, LLM APIs). The team builds and evolves scalable microservices for BMLL Lab & Vantage, the company’s SaaS products for data science and visualisation on large financial datasets.

You'll develop systematic trading strategies and alpha-generating signals for DRW's commodities trading desk, working with large datasets, AI/LLMs, and predictive modeling. Responsibilities include building data pipelines, analyzing trends, creating analytical tools, and maintaining post-trade diagnostics across strategies. Requires a degree in mathematics, statistics, physics, engineering, or computer science, plus minimum two years in an alpha-generating role and proven expertise in data cleaning, model deployment, and programming.

Hudson River Trading★4.5 (2)· London

Hudson River Trading's HAIL team seeks an AI Researcher to develop large language models from scratch—including tokenizer training, pretraining, post-training, evaluation design, inference, and live deployment. You'll work across the full model development loop using state-of-the-art research infrastructure with high GPU-to-researcher ratios, supported by engineering and systems teams. The role requires two+ years hands-on LLM training, post-training, or evaluation experience at scale, strong research taste, and proven ability to drive frontier capability gains.

Hudson River Trading★4.5 (2)· London

You'll work on deep learning and foundation models that ingest market data to predict future market states, using GPU clusters with support for PyTorch, JAX, C++, and FPGA/ASIC development. Hudson River Trading's HAIL team develops and deploys these models directly for its trading operations, with researchers having significant autonomy within a small, focused team to improve all aspects from data featurization through architecture design to trading decisions.

LSEG★3.7 (402)· London

FTSE Russell seeks a Quantitative Engineer to build scalable applications supporting index calculations, back-testing, and analytics using Python, SQL, numpy, pandas, scipy, and cloud-native solutions with RESTful APIs and CI/CD pipelines. The role combines software engineering with quantitative finance to develop tools for index monitoring, validation, and rebalancing across equity, fixed-income, currencies, and commodities. Requires 2+ years in quantitative analytics within financial services and an advanced degree in Mathematics, Computer Science, Financial Engineering, Statistics, or Physics.

Hudson River Trading★4.5 (2)· London

Hudson River Trading seeks Python engineers to design and maintain software systems powering their quantitative trading operations. Projects span distributed computing optimization, developer tooling, LLM applications, system profiling, and trading venue integration. Candidates need strong programming fundamentals, data structures knowledge, and experience at top-tier finance or technology firms.