Machine learning engineer
Find out what a machine learning engineer in government does and the skills you need to do the role at each level.
Last updated 30 November 2024 — See all updates
Contents
- — What a machine learning engineer does
- — Machine learning engineer role levels
- — Roles that share machine learning engineer skills
What a machine learning engineer does
A machine learning engineer develops, assures and maintains machine learning models so they can be used in products and services.
In this role, you will:
- be responsible for the software development and technical infrastructure needed to design, train, deploy and scale machine learning models
- provide and maintain effective, secure and sustainable machine learning models for use in products and services
- support all stages of the machine learning life cycle
- help product teams evaluate and choose appropriate machine learning solutions
Machine learning engineer role levels
There are 2 machine learning engineer role levels, from senior machine learning engineer to lead machine learning engineer.
The typical responsibilities and skills for each role level are described in the sections below. You can use this to identify the skills you need to progress in your career, or simply to learn more about each role in the Government Digital and Data profession.
1. Senior machine learning engineer
A senior machine learning engineer develops machine learning models so they can be used in products and services.
At this role level, you will:
- decide what model is most suitable for use in products and services
- customise, optimise, re-train and maintain existing models
- deploy models into production, testing and assuring them to ensure they meet performance requirements
- work with others to integrate models with existing systems
- check that models used in live products and services stay safe, secure and continue to work effectively
Skill | Description |
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Applied maths, statistics and scientific practices Level: practitioner Practitioner is the third of 4 ascending skill levels |
You can:
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Communicating between the technical and non-technical Level: practitioner Practitioner is the third of 4 ascending skill levels |
You can:
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Level: practitioner Practitioner is the third of 4 ascending skill levels |
You can:
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Ethics and privacy (data science) Level: practitioner Practitioner is the third of 4 ascending skill levels |
You can:
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Programming and build (software engineering) Level: practitioner Practitioner is the third of 4 ascending skill levels |
You can:
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Level: practitioner Practitioner is the third of 4 ascending skill levels |
You can:
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2. Lead machine learning engineer
A lead machine learning engineer leads the technical development and deployment of machine learning models.
At this role level, you will:
- ead the most complex technical work needed to develop models for use in products and services
- co-ordinate moving a model from the research and development stage to production
- define ways of working across the machine learning life cycle
- identify training needs for machine learning engineers and related roles
- help your team work with other teams and disciplines
- assure the effectiveness of machine learning models in use across the organisation
- define and communicate software standards and guidelines related to ethics, risk and security
Skill | Description |
---|---|
Applied maths, statistics and scientific practices Level: practitioner Practitioner is the third of 4 ascending skill levels |
You can:
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Communicating between the technical and non-technical Level: expert Expert is the fourth of 4 ascending skill levels |
You can:
|
Level: expert Expert is the fourth of 4 ascending skill levels |
You can:
|
Ethics and privacy (data science) Level: practitioner Practitioner is the third of 4 ascending skill levels |
You can:
|
Programming and build (software engineering) Level: expert Expert is the fourth of 4 ascending skill levels |
You can:
|
Level: expert Expert is the fourth of 4 ascending skill levels |
You can:
|
Roles that share machine learning engineer skills
Role | Shared skills |
---|---|
Data scientist | |
Development operations (DevOps) engineer | |
Software developer | |
Analytics engineer | |
Data analyst |
Updates
Published 30 August 2024
Last updated 30 November 2024
30 November 2024
- The skill level descriptions for 'systems integration' have been updated to improve clarity, better meet definitions for skill levels and to ensure consistency across the framework. This allows the skill to be shared with roles previously using the skill 'systems integration (network architect)'.
30 August 2024
- First published.