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- Tasks: Design and optimise high-performance data pipelines for groundbreaking ML research.
- Company: Join a mission-driven AI research organisation focused on animal communication.
- Benefits: Competitive salary up to £160k, fully remote work, and flexible hours.
- Why this job: Build infrastructure that directly enables scientific discovery and innovation.
- Qualifications: 5+ years in backend/infrastructure engineering with strong Python skills.
- Other info: Collaborate with researchers in a dynamic, high-performance environment.
The predicted salary is between 72000 - 108000 £ per year.
Do you want to build infrastructure that enables entirely new scientific discovery? Have you scaled data systems for ML workloads where performance actually matters? Are you ready to own foundational infrastructure at global, research-grade scale?
We’re working with a mission-driven AI research organisation applying advanced multimodal machine learning (audio, spatial, sensor, text, etc.) to understand animal communication. Operating at the intersection of ML research, large-scale data infrastructure, and real-world biological data, this team functions like a high-performance research lab with production-grade engineering standards. They are entering a growth phase and are scaling the core systems that power distributed AI research, large multimodal datasets, and public-facing data platforms. This is not consumer tech or ad optimisation - the infrastructure you build directly enables new scientific discovery.
The role is focused on designing, scaling, and hardening the data and backend platforms that support distributed ML workloads and TB–PB scale datasets. You’ll work closely with researchers and engineers to turn experimental systems into reliable, high-performance, production infrastructure.
Key responsibilities- Design and optimise high-performance data pipelines for large, heterogeneous datasets
- Scale public-facing data infrastructure supporting ML research
- Optimise distributed AI workloads for latency, throughput, reliability, and GPU utilisation
- Build observability tooling for data quality, pipeline health, and experiments
- Support GPU infrastructure for large-scale model training
- Translate research prototypes into robust, production systems
- Scope and supervise work for interns, PhDs, and post-docs
- Salary: up to £150k (may be flexibility to £160k) (UK)
- Working model: Fully remote (UK)
- Tech stack: Python, Kubernetes, Docker, Terraform, GCP, GPU clusters
- Visa: No sponsorship
- Seniority: 5+ years backend / infrastructure engineering (flexible for exceptional profiles)
Senior ML Infrastructure Engineer in England employer: Harnham
Contact Detail:
Harnham Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior ML Infrastructure Engineer in England
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can get your foot in the door.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repo showcasing your projects related to ML infrastructure. We want to see how you’ve tackled real-world problems and made an impact.
✨Tip Number 3
Prepare for those interviews! Brush up on your technical knowledge and be ready to discuss your past experiences. We recommend practising common interview questions and even doing mock interviews with friends.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. We’re excited to see what you bring to the table!
We think you need these skills to ace Senior ML Infrastructure Engineer in England
Some tips for your application 🫡
Show Your Passion for Science: When you're writing your application, let your enthusiasm for scientific discovery shine through. We want to see how your experience in ML infrastructure can contribute to groundbreaking research, so don’t hold back on sharing your passion!
Tailor Your Experience: Make sure to highlight your relevant experience with data systems and ML workloads. We’re looking for someone who has scaled infrastructure at a global level, so be specific about your achievements and the impact they had on performance.
Be Clear and Concise: Keep your application straightforward and to the point. We appreciate clarity, so avoid jargon unless it’s necessary. Make it easy for us to see how you fit into our mission and the role we’re hiring for.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity. Plus, it shows you’re serious about joining our team!
How to prepare for a job interview at Harnham
✨Know Your Tech Stack
Make sure you’re well-versed in the tech stack mentioned in the job description, especially Python, Kubernetes, Docker, and GCP. Brush up on your knowledge of how these tools work together to support ML workloads, as you’ll likely be asked about your experience with them.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in scaling data systems or optimising performance. Use the STAR method (Situation, Task, Action, Result) to structure your answers, highlighting how your solutions led to tangible improvements in previous roles.
✨Understand the Research Context
Familiarise yourself with the intersection of ML research and biological data. Being able to discuss how your infrastructure work can directly impact scientific discovery will show that you understand the mission of the organisation and are genuinely interested in contributing.
✨Ask Insightful Questions
Prepare thoughtful questions that demonstrate your interest in the role and the organisation's goals. Inquire about their current projects, challenges they face in scaling their infrastructure, or how they measure success in their ML initiatives. This shows you’re engaged and thinking critically about the position.
