Jobs at G-Research

Design and develop core components of a distributed storage platform in Rust. Build scalable, resilient systems for large data workloads, collaborate with researchers and engineers on data-access needs, optimize Kubernetes services, integrate Kafka and streaming technologies, and contribute to architecture, testing, code quality, and operational excellence.

Design and develop core components of a distributed storage platform in Rust. Build scalable, resilient services for large data workloads, optimize systems running in Kubernetes, integrate Kafka-based event streaming, and contribute to distributed-systems architecture. Collaborate with researchers and engineers to understand data-access requirements while promoting testing, code quality, operational excellence, and clear technical communication.

Lead the Core AI team and its foundational platform, including GPU compute, model serving, developer tooling, secure AI-agent environments, and MCP services. Own the infrastructure roadmap, build-versus-buy decisions, sandboxing strategy, and developer adoption initiatives. Mentor product-focused engineers, influence senior stakeholders, collaborate with open-source teams, and drive scalable platform delivery across the firm.

The role involves developing machine learning models, optimising system performance, and collaborating with diverse teams on innovative projects.

G-Research seeks a Quantitative Researcher to use machine learning and statistical methods to analyze global financial markets. A Master's or PhD in a quantitative field is preferred, but prior finance experience is not necessary.

Build production data pipelines that transform third-party datasets into research-ready data and live signals. Partner with quantitative researchers to engineer features, develop signals, validate and normalize data, and use LLM-based agents to automate ingestion-to-signal workflows. The role also involves creating reusable tools that help researchers and data scientists scale their work.

Own recurring operational and productivity reporting, deliver ad hoc analysis for senior managers, support process redesign, maintain operational hygiene and resilience, identify emerging issues, and build relationships across technology teams.

Build, maintain, and evolve RHEL and Ubuntu platforms supporting quantitative research. Responsibilities include OS image creation, provisioning and lifecycle management, Ansible automation, Linux security hardening, vulnerability response, kernel and package remediation, observability, telemetry, APIs, databases, and agentic frameworks for autonomous operations. The role requires strong Linux expertise, programming ability, infrastructure automation skills, and collaboration.

Build and operate platform services, APIs, workflows, and data models for storage infrastructure. Automate capacity, tenant, quota, access, and data lifecycle processes with safe, auditable, idempotent changes. Develop tests, observability, and stable service contracts while troubleshooting production systems, responding to incidents, and participating in on-call rotation. Collaborate with users and partner teams to convert operational challenges into reliable engineering solutions.

Twelve-week machine learning internship focused on implementing research papers, building custom libraries for petabyte-scale model training, optimizing ML operations, profiling architectures, and evaluating emerging ML hardware and software. Interns will collaborate with quantitative researchers and engineers on advanced machine learning and scientific computing challenges.

Graduate machine learning engineers will work with quantitative researchers on advanced machine learning and scientific computing problems. Responsibilities include implementing published research, developing custom libraries for large-scale model training, optimizing machine learning operations, profiling architectures, identifying performance bottlenecks, and evaluating ML hardware and software. Candidates should have strong object-oriented programming and machine learning skills, with experience in Python, PyTorch, NumPy, optimization, HPC, profiling, or model inference.

A 12-month HR placement supporting HR operations, benefits, payroll coordination, employee records, reward, talent acquisition, campus hiring, marketing communications, events, and employer branding. Responsibilities vary by rotation and include managing HR projects, coordinating external providers, analyzing reward data, sourcing technical talent, supporting university partnerships, and contributing to recruitment and communications initiatives.

Twelve-week software engineering internship developing production-level code, research platforms, data-processing pipelines, database queries, and internal tools supporting quantitative finance research. Interns collaborate with quantitative researchers and experienced engineers, contribute to scalable systems, and receive structured onboarding, mentoring, and technical development opportunities.

Build and lead the Platform Desktop Engineering team, owning service availability, security, performance, support, roadmap, provisioning, image builds, automation, monitoring, hardware lifecycle, capacity, and budget. The role requires hands-on Linux expertise, production service ownership, enterprise security judgment, stakeholder management, and collaboration across infrastructure, security, identity, support, and HR teams.

Maintain the Linux desktop platform, including OS image build pipelines, provisioning tooling, security, monitoring, hardware lifecycle, and system health. Troubleshoot bare-metal and virtual machine provisioning across OpenStack and Ironic, support technical users, manage incidents and changes, and contribute to capacity planning. The role requires strong Linux administration, Python scripting, infrastructure automation, security, diagnostics, and communication skills.

Recruit exceptional software engineering talent through proactive sourcing, market mapping, networking, referrals, and relationship-building. The role manages long-term talent pipelines, partners with engineering leaders and hiring managers, champions candidates, informs recruitment strategy, and contributes to employer branding and candidate experience improvements.

Design, build, and optimize scalable software platforms supporting quantitative research and financial trading. Responsibilities include developing trading systems, modeling and simulation frameworks, research tools, machine-learning infrastructure, and high-performance distributed systems. The role involves collaborating with quantitative researchers, owning solutions end to end, applying strong software fundamentals, improving scalability and performance, and evaluating emerging technologies.

Own the reliability, performance, resilience, observability, automation, and security of enterprise network infrastructure. Design and maintain Cisco and Arista datacenter networks, support WAN connectivity, automate operations with Python and Ansible, extend infrastructure-as-code and CI/CD practices, and lead incident response, on-call operations, postmortems, and root-cause analysis for complex network issues.

As an Observability Engineer, you'll design systems for telemetry management, build scalable pipelines, and contribute to industry observability standards. Responsibilities include extending OpenTelemetry and enhancing incident response with telemetry data.

Lead and shape the Adversary Emulation (internal Red Team) programme: plan and run complex offensive security operations, provide technical leadership and stakeholder reporting, develop tooling and team capabilities, and coach team members to improve organisational security outcomes.