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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
9
Salary range
£44k – £111k
Hiring companies
7
View 9 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

£44k – £111k

Based on advertised midpoints across the 4 priced listings posted in the last 12 months. Base salary only.

Salary visibility
19% of listings advertise a salary.
Skills & tools

What hiring managers ask for

% of 8 listings posted in the last 12 months that mention each skill, extracted from job descriptions.

Python
75%
CI/CD
75%
Kubernetes
63%
AWS
63%
GCP
63%
Terraform
63%
Azure
63%
Linux
38%
Networking
25%
Rust
25%
Go
25%
Distributed Systems
25%
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

9 live roles

See all 9 roles →
Synthesia logo

Infrastructure Engineer

This role involves maintaining and scaling Kubernetes clusters, managing AWS and GCP cloud environments, and improving CI/CD systems. You will also focus on observability, FinOps practices, and collaborating with product engineers to deploy and monitor production services.

Synthesia London, United Kingdom
Remote Permanent

Staff+ Infrastructure Engineer, Cluster Infrastructure

As a Staff Infrastructure Engineer, you will lead the technical direction for cluster lifecycle management, ensuring reliable and scalable compute infrastructure. You will collaborate with cross-functional teams to drive innovation in cloud and on-premises solutions, focusing on security, performance, and operational excellence.

Anthropic London, United Kingdom
On-site Permanent
OpenAI logo

Software Engineer, GPU Infrastructure- ChatGPT Engineering

Design and operate software systems managing large-scale GPU clusters that power ChatGPT inference workloads. Build automation, observability, and intelligent tooling to improve fleet reliability, efficiency, and scalability. Collaborate with infrastructure, research, and product teams to optimize compute utilization and drive operational excellence across distributed systems.

OpenAI London, United Kingdom
Hybrid Permanent
PhysicsX logo

Principal Machine Learning Infrastructure Engineer

About us PhysicsX is a deep-tech company with roots in numerical physics and Formula One, dedicated to accelerating hardware innovation at the speed of software. We are building an AI-driven simulation software stack for engineering and manufacturing across advanced industries....

PhysicsX London, United Kingdom

GCP AI Infrastructure & Cloud Engineer - London

This role involves building and managing secure, scalable cloud environments on GCP and Azure to support enterprise AI workloads, including model hosting, vector databases, and LLMOps pipelines. The engineer will collaborate with AI, security, and compliance teams to ensure robust, governed platforms while driving cost optimisation and operational efficiency. Key responsibilities include configuring API gateways, maintaining sandbox environments, and implementing automation for AI infrastructure.

Tenth Revolution Group London, United Kingdom £90,000 – £115,000 pa
Hybrid Permanent
OpenAI logo

Software Engineer, ChatGPT Infrastructure

Design, build, and operate reliable, scalable systems supporting AI products like ChatGPT and the OpenAI API. Focus on performance optimization, system resilience, automation, and developer experience across distributed infrastructure. Collaborate with research, product, and engineering teams to improve reliability, scalability, and observability at global scale.

OpenAI London, United Kingdom
On-site Permanent

Forward Deployed Engineer, Infrastructure Specialist (Europe/Middle East)

This role involves leading the end-to-end deployment of Cohere's North AI platform in private cloud and on-premises environments. You will partner with enterprise IT teams to assess infrastructure, security, and data management practices, and design deployment strategies that meet client needs while ensuring compliance with data privacy and security standards.

Cohere United Kingdom
Remote Permanent

Staff+ Software Engineer, Safeguards Infrastructure

This role involves developing foundational systems for AI safety, including data storage, metric evaluation, and human review tools. The focus is on building robust defenses to prevent misuse and ensure user well-being, with a strong emphasis on operational reliability and scalability.

Anthropic London, United Kingdom £255,000 – £325,000 pa
Hybrid Permanent
See all 9 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
8 open

Southampton

Location
2 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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