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.

Work as a Python software engineer on the Risk team, building and delivering solutions using Python, REST APIs, and modern software architecture with cloud platforms such as AWS, Kubernetes, Docker, or Helm. You will address requirements from the Risk Management team and Trading desks at a tech-driven quantitative and systematic investment manager.

Work with Terraform, Ansible, Kubernetes, Python, Bash, Go, Jenkins, GitHub Actions, Prometheus, and Grafana to build infrastructure automation platforms supporting python-based quantitative research and trading systems. Manage on-premises resources across Ubuntu and Windows Server environments.

Builds cloud platforms for a systematic quant hedge fund's trading and research, using AWS (EC2, S3, VPC), Python/Bash, Jenkins/GitLab CI, Terraform/CloudFormation/AWS CDK, Docker, and Kubernetes to support digital asset and crypto systems.

You will work with Linux, AWS, Docker, Kubernetes, CI/CD pipelines, Python, Bash, and monitoring tools like Datadog, Prometheus, CloudWatch, or ELK. You will support and maintain the critical research and trading infrastructure for a quantitative hedge fund, acting as first-line support for platform-related issues.

You will work with Go and/or Python, plus Kubernetes, Docker, and cloud infrastructure. You'll support a systematic cryptocurrency trading team by maintaining live production trading and research systems, and helping to deploy strategies.

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.

Build and own the cloud infrastructure for live AI-driven trading systems, using AWS, Kubernetes, Terraform, Python, Kafka, PostgreSQL, Redis, and ClickHouse. You will design deployment pipelines, multi-region environments, and observability with Grafana, Prometheus, and Loki. This is a founding engineering role, giving you direct ownership of the platform and architecture.

Man Group★3.6 (14)· London

Lead development of Man Group’s core Python compute platform, working with Kubernetes, Spark, Hadoop Yarn, Ray, and Airflow on Linux for systematic and discretionary investment strategies. You will build the research platform and APIs for researchers and developers.

Jump Trading★3.8 (11)· London

As a core development intern, you will design, code and test the firm's distributed trading system using C++ and Python on Linux, with Git for version control, and gain experience with Agile methodologies. The team builds and maintains Jump Trading's world-class low-latency trading platform and infrastructure.

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.

AllianceBernstein★3.5 (269)· London

You will work with Python, SQL, Airflow, and Kubernetes, using the abAlphaLabs research platform for systematic strategies. The team develops, implements, and manages fully systematic, factor-driven fixed-income portfolios.

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.

Tower Research Capital★4.1 (12)· London

You will support and maintain Tower Research Capital's Linux infrastructure for high-frequency, low-latency electronic trading systems, working with technologies including AWS (EC2, VPC, ELB, S3, EBS), Ansible, Python, Bash, Docker, and Kubernetes. Responsibilities include managing core services (DHCP, LDAP, DNS, NFS), automating provisioning and monitoring across thousands of servers in on-premises and cloud environments, and participating in on-call rotations supporting 24/7 critical trading infrastructure. You'll need at least five years of Linux administration experience, deep knowledge of OS internals and networking, and expertise in infrastructure automation and CI/CD practices.

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.

JPMorgan★3.9 (20k)· London

You'll work with Python, KDB/C++, and AI technologies to design and optimize real-time data processing pipelines for JPMorgan's Electronic Trading Technology team within Commercial & Investment Bank. You'll lead technical initiatives across global analytics teams, building scalable solutions for mission-critical trading and research systems while mentoring engineers and driving SDLC improvements. This is a leadership role managing distributed teams in a fast-paced financial environment.

G-Research★3.6 (18)· London

You'll design and operate Kubernetes-based infrastructure using Go or Python to build custom operators, GitOps pipelines (ArgoCD, FluxCD), and Infrastructure as Code (Terraform, Helm), with strong observability via Prometheus and Grafana. The platform supports G-Research's quantitative finance operations, serving GPU-intensive machine learning and ultra-low-latency trading workloads while enforcing multi-tenant security with RBAC and OPA/Gatekeeper.

Tower Research Capital★4.1 (12)· London

You'll work with C++, Rust, Python, and large-scale build systems like Bazel, Buck2, and CMake to design developer tools, CI/CD pipelines, and infrastructure automation. Tower Research Capital is a quantitative trading firm where you'll partner across teams to enhance platform tooling, optimize build performance, and establish DevOps best practices for a high-performance electronic trading infrastructure.

You will develop production Python systems with SQL and modern databases to build data pipelines handling terabytes of exchange traffic in real-time for Maven Securities, a proprietary trading firm. The role involves designing analytics and data infrastructure supporting traders and quant researchers across discretionary, systematic, and market-making strategies in global listed derivatives. Ideal experience includes large-scale data engineering, streaming pipelines, containerization, C++, React/TypeScript, and financial markets knowledge.