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ML Platform Engineer Jobs

Engineers who build and maintain the infrastructure that powers machine learning workflows. A critical role in scaling ML operations from prototype to production.

Open roles
17
Salary range
£50k – £118k
Hiring companies
12
View 17 live roles → Get an alert

ML Platform Engineers are the backbone of any organisation that relies heavily on machine learning. They design, build, and maintain the infrastructure that supports the entire ML lifecycle, from data ingestion and model training to deployment and monitoring. These engineers work closely with data scientists, ML researchers, and DevOps teams to ensure that ML models can be efficiently and reliably deployed at scale. Whether in a research-heavy startup or a large enterprise, the role is crucial for turning theoretical models into practical, production-ready solutions.

What the role does

Inside the role of an ML Platform Engineer

A typical week is split between developing and maintaining infrastructure, collaborating with cross-functional teams, and ensuring the smooth operation of ML workflows.

  1. 01
    Design and implement scalable ML infrastructure.
  2. 02
    Collaborate with data scientists to optimise model training pipelines.
  3. 03
    Monitor and troubleshoot production ML systems.
  4. 04
    Integrate new tools and technologies into the ML platform.
  5. 05
    Document and maintain system architecture and processes.
  6. 06
    Participate in code reviews and contribute to team knowledge sharing.
Salary on the board

£50k – £118k

Based on advertised midpoints across the 9 priced listings posted in the last 12 months. Base salary only.

Salary visibility
12% of listings advertise a salary.
By seniority
£k base
Senior
50
134
5 jobs
Skills & tools

What hiring managers ask for

% of 24 listings posted in the last 12 months that mention each skill, extracted from job descriptions.

Python
71%
Kubernetes
67%
Terraform
67%
CI/CD
50%
AWS
42%
GCP
33%
Distributed Systems
25%
Azure
25%
Go
25%
Docker
25%
GitHub Actions
21%
Grafana
17%
Career ladder

From Junior to Principal

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

  1. Level 1

    Junior ML Platform Engineer

    0–2 yrs

    Assists in the development and maintenance of ML infrastructure, focusing on learning and contributing to smaller projects.

  2. Level 2

    ML Platform Engineer

    2–5 yrs

    Takes ownership of specific components of the ML platform, ensuring they are scalable, reliable, and efficient.

  3. Level 3

    Senior ML Platform Engineer

    5–8 yrs

    Leads the design and implementation of complex ML infrastructure, guiding junior engineers and driving innovation.

  4. Level 4

    Principal ML Platform Engineer

    8+ yrs

    Strategises and oversees the entire ML platform, influencing organisational direction and leading major initiatives.

Pathway

How to become a ML Platform Engineer

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

  1. 1

    Learn the Basics

    Gain foundational knowledge in ML, DevOps, and cloud computing. Start with small projects to understand the ML workflow.

  2. 2

    Build Practical Skills

    Work on real-world projects, focusing on developing and maintaining ML infrastructure. Collaborate with data scientists and DevOps teams.

  3. 3

    Specialise in ML Platforms

    Deepen your expertise in ML platform engineering, including advanced topics like MLOps and model deployment.

  4. 4

    Lead Projects

    Take on leadership roles, overseeing the design and implementation of large-scale ML infrastructure projects.

  5. 5

    Influence Strategy

    Contribute to the strategic direction of the organisation, driving innovation and efficiency in ML operations.

  6. 6

    Mentor and Innovate

    Mentor junior engineers, foster a culture of continuous learning, and lead cutting-edge research and development in ML platforms.

Live jobs

17 live roles

See all 17 roles →
Isomorphic Labs logo

Senior Software Engineer (Inference Platform), London

This role involves leading the reliability and scalability of AI/ML infrastructure for drug discovery, focusing on GPU/TPU systems, Kubernetes orchestration, and inference services. The engineer will design test harnesses, improve monitoring and CI/CD stability, and ensure high-throughput model serving. Work is closely aligned with research and applied ML teams to support biotech R&D at digital speed.

Isomorphic Labs London, United Kingdom
On-site Permanent
Wayve logo

Embedded Automotive Platforms Software Engineer

Develops embedded software platforms that interface with automotive hardware and AI driving models, creating abstractions and APIs to enable consistent deployment across diverse vehicle systems. Works across Linux and QNX environments, integrating inference, control, and vehicle interfaces while minimizing platform-specific customizations. Collaborates with hardware partners and cross-functional teams to ensure robust, scalable autonomous vehicle software.

Wayve London, United Kingdom
Hybrid Permanent
See all 17 roles →
Top hirers

Companies hiring ml platform 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
13 open

Manchester

Location
1 open
FAQs

Common questions

  • Essential skills include proficiency in programming languages like Python and Java, knowledge of cloud platforms (AWS, GCP, Azure), and experience with DevOps tools and practices.

  • ML Platform Engineers collaborate closely with data scientists to understand their needs, optimise model training pipelines, and ensure that models can be deployed and scaled effectively.

  • Key challenges include managing the complexity of ML workflows, ensuring high availability and performance of ML systems, and keeping up with rapidly evolving technologies and best practices.

  • Career progression typically involves moving from hands-on technical roles to leadership positions, where you can influence the strategic direction of ML operations and mentor junior engineers.

  • Salary ranges can vary widely based on experience, location, and company size. For more detailed information, please refer to the salary section on this page.

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