> Markdown version of [/jobs/ext/1434762-senior-research-engineer-ml-systems](https://www.wearedevelopers.com/jobs/ext/1434762-senior-research-engineer-ml-systems). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Research Engineer, ML Systems - **Company:** Basecamp Research Ltd - **Location:** UK - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Artificial Intelligence, Airflow, Computational Biology, Nvidia CUDA, Open Source Technology, Tensorflow, Software Engineering, Deep Learning, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Machine Learning Operations, Data Pipelines - **Published:** July 25, 2026 - **Apply:** https://www.totaljobs.com/job/senior-research-engineer/basecamp-research-ltd-job107747880 ## About the Role PhD in computer science, physics, mathematics, or a related field, or equivalent depth of experience gained through years of building ML systems at scale. You have worked at a frontier research lab where research engineers are treated as first-class contributors. You understand how to run large-scale experiments and you've been part of making that actually happen at scale. You are comfortable across the full stack of ML systems: distributed training frameworks, GPU/accelerator optimization, data pipelines, experiment tracking, and making research reproducible and reliable. You may have experience down to the level of CUDA kernels, and/or you may operate more at the framework and orchestration layer. You care about research outcomes as much as system uptime. You form opinions about what experiments to run and how to design them. You can read papers and research reports and figure out what it would take to implement them as efficiently as possible. A backround in mathematics or physics is strongly preferred. The best research engineers bring quantitative intuition to system design decisions. You have strong software engineering practices: clean code, good testing habits, and an instinct for building systems that other people can actually use. In a small team, the infrastructure you build is the infrastructure everyone depends on. You are genuinely curious about biology. The data you'll work with encodes billions of years of evolution, drawn from ecosystems most datasets never touch. You should find that interesting, not incidental. Low ego, collaborative instincts, and a startup mentality. You're comfortable with ambiguity and happy to wear multiple hats in a team where everyone contributes beyond their job description. Nice to Have Experience with biological data: genomic sequences, protein structures, molecular data, or similar. Familiarity with our broader tech stack: Kubernetes, Dagster, or similar orchestration and infrastructure tools. Contributions to open-source ML frameworks or research codebases. ## Description We are looking for a Senior Research Engineer to join our AI Research team in London. You will work on the the technology, systems and infrastructure that power our frontier research, from accelerators and distributed training pipelines to experiment frameworks and the tooling that lets a small team operate at scale. You will sit within the research team, understand the science, and make decisions that directly shape what research is possible. The best research engineers change what experiments the team can run and how fast ideas move from whiteboard to result. We train large-scale models on the world's richest biological datasets. The engineering challenges are unique: custom architectures on novel data modalities, training runs that push hardware limits, and a pace of experimentation that demands robust and flexible tooling. You will be the person who makes all of that work reliably, and who figures out how to make it work better. You'll report to the Head of AI Research and work closely with researchers covering genomics, computational biology, and large-scale deep learning. You'll also collaborate with the broader platform engineering and data teams across the company. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Running Secure Life Science Research at Scale using Hybrid GPU HPC and Kubernetes 🧬](https://www.wearedevelopers.com/videos/100355-running-secure-life-science-research-at-scale-using-hybrid-gpu-hpc-and-kubernetes) - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)