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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
15
Salary range
£50k – £122k
Hiring companies
10
View 15 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 – £122k

Based on advertised midpoints across the 8 priced listings posted in the last 12 months. Base salary only.

Salary visibility
11% of listings advertise a salary.
By seniority
£k base
Senior
50
134
5 jobs
Skills & tools

What hiring managers ask for

% of 20 listings posted in the last 12 months that mention each skill, extracted from job descriptions.

Python
75%
Terraform
75%
Kubernetes
65%
CI/CD
50%
AWS
45%
Go
30%
Azure
30%
GCP
30%
Docker
25%
GitHub Actions
25%
Grafana
20%
Distributed Systems
20%
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

15 live roles

See all 15 roles →
Faculty AI logo

Platform Engineer

This role involves designing and maintaining the MLOps and deployment infrastructure that enables data scientists to transition machine learning models from exploration to production. The engineer will work across AWS, Azure, and GCP to build scalable, containerised systems using Kubernetes and infrastructure-as-code tools. The focus is on creating reliable, secure, and high-performance platforms that support client-facing AI solutions.

Faculty AI London, United Kingdom
Hybrid Permanent

Platform Engineer

Design and build secure, scalable cloud infrastructure to support AI and ML workflows in cross-functional teams. Enable data scientists and ML engineers through reusable infrastructure-as-code and best practices. Work with clients to implement cutting-edge solutions using modern DevOps and cloud technologies.

Faculty London, United Kingdom
Hybrid Permanent

Platform Engineer

A Platform Engineer will design and scale Kubernetes-based hybrid compute clusters across private and public clouds, supporting machine learning and data science workflows. The role involves building core platform services, managing CI/CD pipelines, and ensuring system reliability, security, and scalability. Close collaboration with ML and data teams is essential to meet computational demands in a high-impact biotech environment.

Relation Therapeutics London, United Kingdom
Hybrid Permanent
Synthesia logo

ML Platform Engineer

Design and improve platform systems for model training, evaluation, and production serving. Build reliable, scalable infrastructure and tooling for ML workloads, with a focus on automation, observability, and developer experience. Collaborate with researchers and engineers to develop abstractions that reduce operational overhead in GPU and cloud environments.

Synthesia London, United Kingdom
Remote Permanent
Synthesia logo

Principal ML Platform Engineer

Design and build scalable, reliable systems for training, serving, and operating generative AI models in production. Develop internal tooling and agentic workflows to reduce manual effort and improve automation across research and product teams. Collaborate with researchers and engineers to enhance platform observability, debugging, and developer experience at scale.

Synthesia London, United Kingdom
Remote Permanent

Director AI Platform & Engineering

Your new roleThe Director, Agentic AI Platform & Engineering owns the technical platform on which the businesses' AI agents are built and operated: the AWS-based agentic automation stack (Amazon Bedrock / AgentCore and related services). This is a hands-on engineering...

Hays Technology Oxfordshire, United Kingdom £900 – £936 pd

Associate Director, Platform Engineering

Lead the platform engineering function for a TechBio company pioneering drug discovery through single-cell multi-omics and machine learning. Define and evolve the hybrid on-premises and cloud infrastructure strategy, with a focus on Kubernetes, MLOps, scientific compute environments, and platform security. Develop the team and platform to support data-intensive genomic pipelines, model training, and CI/CD maturity while ensuring robust observability, disaster recovery, and compliance with clinical-stage data governance standards.

Relation Therapeutics London, United Kingdom
Permanent
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
See all 15 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
11 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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