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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
6
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 5 listings posted in the last 12 months that mention each skill, extracted from job descriptions.

Python
80%
GCP
80%
AWS
80%
Terraform
80%
CI/CD
80%
Azure
60%
Kubernetes
60%
Linux
40%
PyTorch
20%
Distributed Training
20%
Docker
20%
ML Pipelines
20%
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 →

Infrastructure Engineer

This role involves building and scaling the cloud and on-prem infrastructure that powers ElevenLabs' AI platforms. You will work closely with research, product, and security teams to launch, scale, and improve the reliability and efficiency of Kubernetes clusters, data systems, and cloud infrastructure.

ElevenLabs London, United Kingdom
Remote Permanent

HPC Infrastructure Engineer - GPU Clusters

This role involves operating and improving NVIDIA GPU clusters, including provisioning, scheduling, monitoring, and capacity planning. You'll build automation, optimize performance, and handle hands-on hardware tasks to ensure reliable and fast compute for AI research.

ElevenLabs United Kingdom
Remote Permanent

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

Senior Machine Learning Infrastructure Engineer, Research

This role involves designing and operating distributed training infrastructure for large physics models using NVIDIA DGX B200 systems, with a focus on optimizing training pipelines, data I/O performance, and model serving. The engineer will work closely with research scientists and ML engineers to enable efficient, scalable AI training and deployment in high-performance computing environments, while also building observability and reproducibility into the research workflow.

PhysicsX United Kingdom
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

This role involves building and scaling systems to train highly capable AI agents and deploying them to millions of users. You will work on optimizing large-scale compute clusters, developing APIs, and collaborating with research teams to push the boundaries of AI infrastructure.

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 →
Hiring locations

Where this role is hiring

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

London

Location
8 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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