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ML Infrastructure Engineer Jobs

Engineers who design, build, and maintain the systems that power machine learning workflows. A critical role in scaling and optimising ML operations.

Open roles
14
Salary range
£55k – £223k
Hiring companies
7
View 14 live roles → Get an alert

ML Infrastructure Engineers are the backbone of any machine learning organisation. They design, build, and maintain the infrastructure that supports the entire ML lifecycle, from data ingestion and preprocessing to model training, deployment, and monitoring. These engineers work closely with data scientists and ML researchers to ensure that the infrastructure is robust, scalable, and efficient. They are often found in tech scaleups, research-heavy startups, and the larger consultancies, where the demand for reliable and performant ML systems is high.

What the role does

Inside the role of an ML Infrastructure Engineer

A typical week is split between designing and implementing new infrastructure components, troubleshooting existing systems, and collaborating with cross-functional teams.

  1. 01
    Design and implement scalable data pipelines.
  2. 02
    Optimise and maintain ML model training environments.
  3. 03
    Collaborate with data scientists to understand infrastructure needs.
  4. 04
    Troubleshoot and resolve performance issues in production systems.
  5. 05
    Document and standardise infrastructure best practices.
  6. 06
    Stay updated with the latest ML infrastructure trends and tools.
Salary on the board

£55k – £223k

Based on advertised midpoints across the 6 UK listings priced in pounds in the last 12 months. Base salary only.

Salary visibility
33% of listings advertise a salary.
Skills & tools

What hiring managers ask for

% of 7 listings posted in the last 12 months that mention each skill, extracted from job descriptions.

GCP
86%
CI/CD
86%
Python
71%
AWS
71%
Kubernetes
71%
Azure
57%
Terraform
57%
Machine Learning
29%
Networking
29%
Linux
29%
Distributed Systems
29%
PyTorch
14%
Career ladder

From Junior to Principal

A typical UK progression for ml infrastructure engineers. Years are guidance — strong people move faster, and many senior folks sidestep into research, product or management.

  1. Level 1

    Junior ML Infrastructure Engineer

    0–2 yrs

    Assist in the design and implementation of basic ML infrastructure components, with a focus on learning and gaining hands-on experience.

  2. Level 2

    ML Infrastructure Engineer

    2–5 yrs

    Own the design and implementation of key infrastructure components, ensuring they are scalable and efficient.

  3. Level 3

    Senior ML Infrastructure Engineer

    5–8 yrs

    Lead the development of complex infrastructure solutions, mentor junior engineers, and drive infrastructure innovation.

  4. Level 4

    Principal ML Infrastructure Engineer

    8+ yrs

    Strategise and oversee the entire ML infrastructure architecture, influence company-wide technical decisions, and lead major initiatives.

Pathway

How to become a ML Infrastructure Engineer

There's no single route, but most people follow some version of these steps.

  1. 1

    Learn the Basics

    Start by gaining a solid understanding of data engineering, cloud services, and ML workflows. Hands-on projects and courses can help build a strong foundation.

  2. 2

    Gain Practical Experience

    Work on real-world projects, either through internships or entry-level roles. Focus on building and maintaining data pipelines and ML environments.

  3. 3

    Specialise in ML Infrastructure

    Deepen your expertise in specific areas such as distributed systems, containerisation, and orchestration tools. Contribute to more complex infrastructure designs.

  4. 4

    Lead Projects and Teams

    Take on leadership roles, managing infrastructure projects and mentoring junior engineers. Drive innovation and best practices within your team.

  5. 5

    Influence Company Strategy

    At the senior level, you will influence the overall ML infrastructure strategy, working closely with leadership to shape the technical direction of the organisation.

  6. 6

    Industry Thought Leadership

    Becoming a principal engineer involves not only leading within your organisation but also contributing to the broader ML community through speaking engagements and publications.

Live jobs

14 live roles

See all 14 roles →

Staff Infrastructure Engineer, Cluster Infrastructure

This role involves leading the technical strategy for agent-driven automation in cluster lifecycle management, ensuring secure, scalable, and fault-tolerant compute infrastructure across cloud and on-prem environments. You'll collaborate with research, product, and security teams to shape long-term infrastructure direction, with a focus on high-bandwidth interconnectivity and operational excellence. The position emphasizes mentorship, cross-team alignment, and driving innovation in large-scale cluster provisioning and management.

Anthropic London, United Kingdom £325,000 – £485,000 pa
On-site Permanent

Machine Learning Systems / AI Infrastructure Engineer- Quant / Systematic Trading Firms

Engineer cutting-edge machine learning infrastructure at scale within high-performance quantitative trading environments. Focus spans distributed training, GPU optimisation, low-latency inference, and full-stack ML systems, integrating hardware and software to accelerate research and production deployment. Work closely with researchers to build robust platforms for rapid model iteration and deployment on massive compute estates.

eFinancialCareers London, United Kingdom £250,000 – £700,000 pa
Isomorphic Labs logo

Software Engineer (ML Infrastructure), London

This role involves building and operating a scalable inference platform to serve cutting-edge machine learning models for scientific drug discovery applications. The engineer will focus on distributed systems, Kubernetes-based infrastructure, and production reliability, with responsibilities spanning development, CI/CD, observability, and user support. The position operates within an interdisciplinary AI-driven research environment, requiring deep technical ownership and first-principles thinking.

Isomorphic Labs London, United Kingdom
On-site Permanent

Research Engineer - Data Infrastructure

This role involves building and maintaining large-scale data infrastructure to support cutting-edge AI models, with a focus on data pipelines, curation strategies, and tooling for processing massive datasets. The engineer will develop classifiers and quality filters, design deduplication and augmentation systems, and enable efficient data exploration. The work directly impacts model performance through high-quality, scalable data engineering and infrastructure innovation.

ElevenLabs United Kingdom
Remote Permanent
OpenAI logo

Software Engineer, Compute Infrastructure

This role involves building and optimizing the compute platform that powers OpenAI's AI research and products. Responsibilities include designing and operating infrastructure for large-scale compute systems, profiling and optimizing training workloads, and creating tools that improve the developer experience and system reliability.

OpenAI London, United Kingdom
Hybrid Permanent
OpenAI logo

Software Engineer, Agent Infrastructure

About the TeamThe Agent Infrastructure team at OpenAI is responsible for building systems that enable training and deployment of highly useful AI agents, both internally and for the world.We work hand-in-hand with researchers to design and scale the environment in...

OpenAI London, United Kingdom
Permanent

Performance Engineer (Junior) | AI Infrastructure | Cambridge

This role involves working alongside senior engineers to build performance models and calculators for AI infrastructure, predicting the efficiency and cost-effectiveness of different setups. You'll work with real metrics from live training and inference jobs, using your strong background in computer architecture and experience with GPU code and profiling tools.

Pure Resourcing Solutions Dry Drayton, Cambridgeshire, United Kingdom £55,000 – £70,000 pa
Hybrid

Senior Performance Engineer | AI Infrastructure | Cambridge

This role involves working between research and engineering teams to build performance models and calculators that optimize AI infrastructure. You'll analyze real-world training and inference runs to provide actionable insights that influence hardware and system decisions for the organization and its members.

Pure Resourcing Solutions Dry Drayton, Cambridgeshire, United Kingdom £90,000 – £120,000 pa
Hybrid
See all 14 roles →
Top hirers

Companies hiring ml infrastructure engineers

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Hiring locations

Where this role is hiring

The locations with the most live listings for this role today.

London

Location
9 open
FAQs

Common questions

  • Essential skills include strong programming abilities, knowledge of cloud platforms, experience with data pipelines, and a deep understanding of machine learning workflows.

  • Gain experience in data engineering and cloud services, and start working on ML-related projects. Online courses and certifications can also help bridge the gap.

  • Senior engineers lead the design and implementation of complex infrastructure solutions, mentor junior team members, and drive innovation in infrastructure practices.

  • The typical progression is from Junior to Senior, then to Principal Engineer. Each step involves increasing responsibility and influence over the organisation's technical direction.

  • Salaries vary based on experience and location. For specific salary ranges, please refer to the salary section on this page.

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