Work with TensorFlow or PyTorch, Python, and related computer vision/graphics libraries on neural network architectures (convolutional, recurrent, transformer, diffusion-based). The Visual Computing Group (VCG) researches and develops generative models for game rendering and live video streaming to advance state-of-the-art runtime efficiency, visual quality, and latency.

Lead UI Developer role using TypeScript, React, RxJS, WebSockets/SSE, GraphQL, Protobuf, Flatbuffers, Jest/Vitest, React Testing Library, Playwright, and AG Grid. You will own the front-end architecture for a real-time Single Dealer Platform used by Sales, Trading, and institutional clients globally, building low-latency trading applications for market data, pricing, RFQ, and trade capture.

You'll use Java, Kafka, Flink, SingleStore, HDFS/S3, and Scala to build high-performance, low-latency data systems. The Data Platform team in Global Markets owns the post-trade stack, processing high volumes of data and developing an AI-powered analytics UI/agent framework.

You will work with backend development, databases, APIs, cloud services and production machine learning systems. This is for a growing technology business. No specific project or team size is mentioned.

Goldman Sachs★3.9 (2.3k)· London

Build a high-performance, low-latency data platform for post-trade processing using Java, Kafka, Flink, SingleStore, HDFS/S3, and Scala. You will own the full lifecycle of core systems, designing distributed computation and an AI-powered UI/agent framework to deliver intelligent analytics over large-scale business data.

Hudson River Trading★4.5 (2)· London

You will use Python, C++, Pandas, NumPy, R, or MATLAB to build and maintain predictive trading models. As an algorithm developer, you research and test novel order execution and model training methods, applying

Hudson River Trading★4.5 (2)· London

You will work with Python, Pandas, NumPy, R, and MATLAB to apply statistical analysis and machine learning to vast quantities of market data. The team builds and maintains predictive trading models for the company's high-performance trading infrastructure. You will research, develop, and test order execution methods to increase trading efficiency.

You will use TensorFlow or PyTorch, Python, and CV/graphics libraries, plus 3D engines like Unreal or Unity, to develop generative models for graphics and video. As part of the Visual Computing Group, you'll design neural networks and ML for PlayStation's game rendering and live

Quantitative Researchers at Jane Street build models, strategies, and systems for pricing and trading financial instruments, working with Python, petabytes of data, and a large GPU cluster. They apply statistical and ML techniques, from linear models to deep learning.

Develop core eTrading MI & Analytics platform components using q and kdb+, with CI/CD, TDD/BDD, and an additional programming language. The platform captures, stores, and analyses over 3TB/5 billion daily records from electronic trading venues for risk, quant, trading, and sales stakeholders.

Python micro-services with RabbitMQ and OpenShift/Kubernetes, using SQL Server and Parquet. The London Commodities Risk Technology team builds a front-office distributed platform for trading and risk calculations on assets like oil, gas, power, and exotics, collaborating with traders and quants.

You will work with Python, JavaScript, SQL, Kafka, AWS/Azure, PostgreSQL, SQL Server, ClickHouse, and MongoDB. The team designs and delivers real-time, high-performance trading systems for Energy & Commodities, integrating market data, risk, and front-to-back office workflows.

You will use C/C++, STL, Boost, and Linux to build low-latency algorithmic trading systems. The team develops and supports all aspects of trading strategies, including exchange price feeds, back testing, and production environments for a tech-driven quantitative hedge fund.

You will work with Rust, Java, C++, KDB, event-sourcing microservices, Linux, TCP, and kernel-bypass networking (Solarflare OpenOnload). The High Performance Architectures team builds low-latency application frameworks and supports modernisation for over 100 eTrading teams across Citi.

Build microservices-based event sourcing systems on an in-house low-latency framework using Java, Rust, C++, KDB, and market data technologies within Citi’s London High Performance Architectures team. The team provides application frameworks and modernization support to over 100 teams, with a focus on low-latency algorithmic trading platforms in Markets Technology.

The role involves working with Java (server-side, multithreading), kdb+/q, Python, and CI/CD/testing frameworks. It sits within a specialist team that builds low-latency pricing, eTrading, and eRisk components for Rates and FX products, covering forwards, futures, swaps, bonds, and eCommerce quoting.

Work with Microsoft Fabric, Databricks, Azure, Snowflake, Python, Spark, SQL/NoSQL, PowerShell, Azure Data Factory and Azure DevOps to build a Fabric Data Lakehouse for USS’s Investment Management business, implementing scalable ETL/ELT frameworks across a medallion architecture. Share knowledge across a small team of data engineers and collaborate with architects, business stakeholders and data teams.

Work with Python, DevOps, Linux, Pandas, NumPy, and relational/non-relational databases. You'll join the Vendor Data Group at a global quant firm to build and maintain mission-critical ETL/ELT data integration pipelines and tools for alternative commodity and macro data, collaborating with a trading team.

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.

You'll work with Python (Pandas, Arrow, Spark) and potentially Lakehouse tools (Delta Lake, Databricks, AWS). Joining Global Data Engineering, you provide first-line support for a high-frequency trading market maker, ensuring robust data pipelines and APIs for trading teams.

You will work with Python, Bash, and possibly simple C++ changes/deployment. You will join the market access team to build and support systems that interface with markets, brokers, and data providers for a tech-driven quant hedge fund.

You will build and optimise low-latency trading system components in modern C++ for a fully automated electronic trading platform. You will design real-time infrastructure for market data ingestion, order execution, and exchange connectivity, working with quantitative researchers and traders.

You will work with Python, PowerShell, SQL, OMS/EMS platforms (FlexTrade, Triton, SpiderRock), Linux, Windows, cloud (AWS/Kubernetes), FIX, messaging systems, market data flows, and monitoring tools like Geneos/Zabbix. As part of a global hedge fund’s front-office technology team, you will directly support equity portfolio managers, traders, and analysts to ensure the reliability and performance of business-critical trading systems.

Work with C#, Python, Java, SQL, and AWS (ECS, S3, DynamoDB) to design, build, and maintain TradingHub’s market data environment and analytics. Collaborate with Pricing Quants and Risk Strats to productionise financial markets data for the company’s trade surveillance software.