Machine Learning Engineer
Key Facts
Overview
Machine learning engineers create programs to enable technology to perform actions without explicit programming or direct human interaction, for example, self-driving cars, using data structures, algorithms and computer architecture. The role of the machine learning engineer is normally quite well-defined due to it being one specialist part of artificial intelligence (AI).
The engineer must predict what will happen in new situations. This is determined through data modelling, ultimately helping them to identify patterns in datasets. They use their coding skills and statistical acumen, alongside this pattern identification, to create a program which predicts an output. For example, this output could be self-completing coupons or online recommendations from retailers.
The machine learning engineer’s contribution is just a small part of the final product; they also liaise with clients and/or management to find out the scope of the project, and with other engineering teams, data scientists, product managers and business operation colleagues.
Machine learning engineers work for AI specialist firms as well as established and start-up technology/software companies. They are also employed by organisations in a range of sectors including engineering, medicine, finance and communications.
Engineers have excellent mathematical and communication skills, can pay attention to detail, and are innovative and creative.
What it takes
What it takes:
- exceptional analytical, logical, statistical and mathematical thinking skills
- the ability to use coding and computer languages and experience in data science
- to be thorough and pay attention to detail
- the ability to come up with new ways of doing things
- use your initiative and complex problem-solving skills
- thinking, judgement and reasoning skills
- knowledge of computer operating systems, hardware and software
- persistence and determination
- knowledge of systems analysis and development
- to have a thorough understanding of computer systems and applications
Day to day
Day to day:
Your day-to-day tasks could include:
- implementing, deploying and maintaining machine learning models, algorithms, tools and frameworks to allow programmes and computers to work independently
- finding patterns in the data to predict the unknown, modelling this data
- building and ensuring the scalability of data pipelines
- coming up with your own original ideas or working from an existing concept
- creating performance metrics and tracking processes to measure the effectiveness of data solutions across different datasets and distribution shifts
- investigating model failures and finding solutions to these
- storing, processing and manipulating data
- collaborating with data engineers, data scientists and information architects to build robust programmes
You will need
- A GCSE (4+/A*-C) (or equivalent) in English
- A GCSE (4+/A*-C) (or equivalent) in maths
- A degree in a numerate or IT subject
You may need
- An A Level (or equivalent) in a numerate or IT subject
Essential Skills
Essential skills typically required to be successful in this career. Click on each skill to find out more.
Academic and Vocational Notes
Most machine learning engineers have a degree in a related area such as computer science, data science or software engineering, however many employers also look for a postgraduate qualification in a subject that includes machine learning or in a closely related area such as artificial intelligence or data science. Always check course content and entry requirements with individual institutions as they vary.
Apprenticeships can lead to further training in machine learning; vacancies are advertised locally, with training and course providers and on the Government's apprenticeships website.
It may also be possible to enter this career with evidence of experience and through self-teaching via online courses.
Work Based Training Notes
It is possible to progress into this career with experience in similar roles such as in data science or software engineering or development; some additional learning may be required.
Rapid advancements in technology requires people in this work to be continuously updating their skills and knowledge.
Other Routes
Whilst these are the usual routes to this career, there can be alternatives. You will be able to discuss these with your adviser.
