You will work with OpenMM, OpenFE, molecular dynamics, MM/GBSA, free-energy perturbation (RBFE/ABFE), and GPU infrastructure to define and validate automated biophysics tools. The team builds a scalable platform for agentic drug discovery workflows, ensuring scientific correctness in molecular simulation.

This role involves working with Python, PyTorch, JAX, CUDA, Linux, Docker, Kubernetes, Slurm, Ray, Flyte, and Argo. The team builds ML and physics-based infrastructure for drug discovery, including simulation workflows and agent-usable scientific pipelines. Engineers improve code quality, GPU efficiency, and distributed execution while making research tools scalable and maintainable.

Work with Python, LLMs, RAG pipelines, vector databases, Hugging Face Transformers, LangChain, PyTorch, and Docker to build production AI systems. Design, develop, and deploy AI-powered solutions such as conversational AI, document intelligence, and backend services for the Global Analytics team.

Develop backend services using Node.js, Python, Java, C#, PHP, and Ruby on Rails, with databases including MySQL,

Trainees will learn Linux, Git, Docker, AWS, Azure, Google Cloud, CI/CD, and Infrastructure as Code. The program provides structured training and practical DevOps projects to build a portfolio. No prior experience is required.

Full-stack Java development on Linux/Unix, using AI coding tools like Claude Code, Windsurf, Devin, or GitHub Copilot. The role is for Ascendion, a technology services company.

The role involves developing embedded software and software for high-speed network platforms, working with GPU-based infrastructure and interconnect technologies. The company builds next-generation networking solutions integrating hardware and photonics for high-performance computing systems.

You will work with C++, Linux, and real-time frameworks to design, develop, integrate, and verify software for high-performance real-time systems. The team builds and supports advanced test and data capture systems for the defence sector.

Hudson River Trading★4.5 (2)· London

You will write and maintain critical trading infrastructure using C/C++ or Python on Linux, working closely with engineers who design trading algorithms in a fast-paced, real-time environment. The role is with Hudson River Trading, a firm that brings a scientific approach to trading financial products.

Elsevier★3.8 (769)· London

Work with Java/J2EE, JavaScript, JSP, C/C++, HTML, XML, SQL, Windows, UNIX, .Net, and various data storage subsystems. You'll develop complex software solutions supporting educational electronic health record products that prepare students for digital charting in clinical environments.

RELX Group★3.8 (133)· London

You will work with Java/J2EE, JavaScript, JSP, C/C++, HTML, XML, SQL, Windows, UNIX, and .Net. The team supports products for education electronic health records that introduce students to digital charting and prepare them for clinical documentation.

This IT Operations internship at Jane Street involves hands-on support, configuration, and operation of systems across desktop engineering, network, and application platforms. You'll build and configure Windows and Linux devices, support Windows domain infrastructure, monitor systems, and use tools like PowerShell. The role requires knowledge of Windows and computer hardware, with scripting familiarity (PowerShell or Bash) a plus.

Linux Engineers at Jane Street work with on-prem and cloud infrastructure, Linux, Nix, RPMs, and open-source tools across compute, storage, and networking. The role maintains the critical infrastructure underlying the firm’s technology, building in-house tools and automating systems.

DeepL★3.8 (4)· London

Work with Kubernetes, Terraform, ArgoCD, Go or Python, and AWS across hybrid on-prem and cloud infrastructure. The Hybrid Platform Engineering track builds and operates the compute layer—Kubernetes control plane, networking, storage, and core services—powering DeepL's language AI products and research. The role involves deepening the hybrid model and unifying workloads across on-prem and AWS.

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.

Senior Machine Learning Engineer at ASOS, building production ML systems for personalized outfit recommendations and style discovery. You'll work with deep learning frameworks like PyTorch/TensorFlow, distributed GPU infrastructure, and MLOps practices including CI/CD, containerization, and observability. Collaborate with ML Scientists on batch and real-time models serving millions of customers.

Use deep learning frameworks, distributed computing, GPU-based training, and MLOps to build and deploy batch and real-time ML models. The Search & Recommendations team develops personalised recommendations, product ranking, and AI styling experiences for ASOS's online fashion platform, helping customers discover products.

You’ll train ML models, work on ML libraries and workflows, and use a GPU cluster with H100/H200/B200s. Projects are real-world machine learning applications for Jane Street’s global trading business, testing ideas against noisy financial data. No team size is stated.

You'll work across the full stack using Python, frontend, embedded systems, ML, and networking, with Linux, devops, and edge-device deployment. Minerva Defence builds software, hardware, and networking for unmanned battlefield systems.

Société Générale★3.8 (3.8k)· London

Support Windows/Linux, SQL, Python, or C# while monitoring and troubleshooting critical trading applications used by foreign exchange and fixed income teams. You'll assist traders and sales desks, resolve technical issues, and contribute to system improvements within Société Générale's Trading Technology team.

Python and C++ (C++17 or later), SQL, and Linux. The team builds and enhances a cutting-edge quantitative trading platform for a world-leading hedge fund, working alongside senior PMs, quants, and engineers across major financial markets.

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 C++, Python, Linux, machine-learning libraries, Google Cloud, Airflow, InfluxDB, and Grafana. This role builds data engineering and prediction tools for systematic equity trading, developing software for quantitative research, maintaining data pipelines, and implementing large-scale computation and serialization frameworks.

Develop, optimize and maintain high-performance C++ code for ultra-low-latency trading systems, working on strategy interaction with FPGA. Collaborate with infrastructure and network teams. Requires deep metaprogramming and 7+ years' C++ experience, with knowledge of memory management, multithreading, CPU cache optimization, and Linux.

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.