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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
19
Salary range
£50k – £118k
Hiring companies
11
View 19 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
13% of listings advertise a salary.
By seniority
£k base
Senior
50
130
6 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
60%
AWS
45%
CI/CD
45%
Go
30%
Azure
30%
Docker
25%
GitHub Actions
25%
GCP
25%
Grafana
20%
Kafka
15%
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

19 live roles

See all 19 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
W

Platform Engineer, AI Enablement

This role involves building and operating secure, scalable infrastructure to enable safe and cost-effective access to AI models and agentic workflows across the company. You'll design governed model-access layers, implement observability and safety controls, and create reusable platform primitives. The position requires close collaboration with security, IT, and engineering teams to support AI adoption while ensuring compliance, reliability, and operational excellence.

Wayve London, United Kingdom

Platform Engineer (DevOps / MLOps Focus)

Design and maintain cloud-native infrastructure to support large-scale AI and machine learning workloads, with a focus on Kubernetes, Terraform, and platform automation. Collaborate with engineering and data science teams to optimise deployment workflows and improve platform reliability, scalability, and observability for production AI systems.

The Portfolio Group London, United Kingdom £100,000 pa
Hybrid Permanent
W

Senior Platform Engineer

This role involves designing and maintaining cloud infrastructure, CI/CD pipelines, and developer tooling on Azure using Terraform, with a focus on RBAC/IAM, Kubernetes, and platform security. The engineer will support a high-autonomy team deploying Wayve’s autonomy stack to OEM partners, building systems that improve developer velocity and deployment safety. Emphasis is placed on internal tooling, observability, and a product mindset to enhance developer experience.

Wayve 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
See all 19 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
14 open

Cambridge

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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