Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Research Technology Developer

Campbell North
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer - Market Intelligence

Senior Data Engineer - Market Intelligence

KDB Developer - Cross-Asset Data Engineering - Banking

Treasury Data Engineer

Data Engineer (Multiple Roles) - AI SaaS

Data Engineer (Multiple Roles) - AI SaaS

Company Overview:

Our client is a research-driven organisation led by passionate mathematicians and computer scientists. The Research Technology team lies at the heart of the company, managing one of the largest HPC clusters in the world. This team is critical to the firm's success, facilitating trades with daily volumes exceeding $250 billion globally.

Team Overview:

The Research Technology team is a full-stack team that collaborates closely with researchers to develop a highly performant, reliable, and transparent system. The team builds custom software to support an exa-scale filesystem, job scheduler, and zero-touch platforms for seamless integration with data centre operations. They are also responsible for developing custom file formats, compression algorithms, GPU tooling, and network management software to optimise performance.

Key Responsibilities:

  • Design and build software for the HPC cluster, focusing on performance, reliability, and scalability.
  • Mentor junior team members and push the boundaries of the team’s capabilities.
  • Engage constructively with researchers to find novel and scalable solutions.
  • Promote and implement radical changes and alternative ways of thinking while maintaining a pragmatic approach to minimise operational risks.
  • Manage and maintain a complex live system 24/7, delivering changes on short notice or tight deadlines.

What You Will Be Working On:

  • Developing an exascale filesystem handling billions of directories, a trillion files, and a million clients with complete resiliency against hardware failure.
  • Enhancing a dynamic job scheduler managing over 10 million entries and 100,000 concurrent tasks.
  • Building zero-touch platforms for monitoring, operating, and upgrading tens of thousands of machines.
  • Creating custom file formats, compression algorithms, and GPU tooling to optimise performance from 20,000 high-end GPUs.
  • Expanding the HPC cluster to provide access to more teams and multiple data centres.
  • Improving measurement and optimisation of resource usage across the entire cluster.

Essential Attributes:

  • Strong academic grounding in computer science fundamentals, including algorithms and data structures.
  • Proficiency in at least one statically typed language; experience with Golang and Rust is beneficial but not required. Scripting is primarily in Python.
  • Approximately 5-10 years of experience in designing and building large-scale distributed systems with highly scalable solutions.
  • Excellent problem-solving and analytical skills.
  • Familiarity with the Linux operating system, particularly in diagnosing performance and scalability issues.
  • Ability to multitask, manage multiple projects simultaneously, and prioritise effectively.
  • High self-motivation and the ability to work independently without supervision.
  • Understanding machine learning frameworks and compute offload devices, such as GPUs, is an advantage.

This role offers the opportunity to work in a fast-paced, research-driven environment where you can significantly impact the firm’s HPC infrastructure and overall success. We encourage you to apply if you are a self-starter passionate about developing cutting-edge technology.

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Machine Learning Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.