Machine Learning Engineer Salary in United Kingdom during 2024

Salary details

The average Machine Learning Engineer salary lies between USD 100,000 and USD 186,875 in the United Kingdom. It represents the overall compensation/gross salary amount for the working year (before deductions like social security, taxes and other contributions), not including equity/stock options or similar benefits.

Region represents the primary country of residence of an employee during the year (or residence for tax purposes). All data shown are full-time equivalent (FTE) salaries. Part-time salary information has been extrapolated to its FTE value.

Last updated: Nov. 10, 2024

Salary trend

Top 20 Job Tags for Machine Learning Engineer roles

The three most common job tag items assiciated with Machine Learning Engineer job listings are Machine Learning, Python and Engineering. Below you find a list of the 20 most occuring job tags in 2024 and the number of open jobs that where associated with them during that period:

Machine Learning | 5306 jobs Python | 4180 jobs Engineering | 3946 jobs Computer Science | 3293 jobs ML models | 2850 jobs PyTorch | 2721 jobs TensorFlow | 2515 jobs Deep Learning | 2129 jobs Research | 2128 jobs Pipelines | 2062 jobs AWS | 1871 jobs NLP | 1809 jobs Statistics | 1706 jobs LLMs | 1667 jobs Architecture | 1556 jobs Testing | 1369 jobs Spark | 1265 jobs SQL | 1225 jobs Mathematics | 1176 jobs PhD | 1143 jobs

Top 20 Job Perks/Benefits for Machine Learning Engineer roles

The three most common job benefits and perks assiciated with Machine Learning Engineer job listings are Career development, Health care and Equity / stock options. Below you find a list of the 20 most occuring job perks or benefits in 2024 and the number of open jobs that where offering them during that period:

Career development | 4627 jobs Health care | 2675 jobs Equity / stock options | 2191 jobs Flex hours | 1591 jobs Flex vacation | 1563 jobs Startup environment | 1384 jobs Parental leave | 1338 jobs Competitive pay | 1299 jobs Salary bonus | 1283 jobs Medical leave | 1256 jobs Insurance | 1216 jobs Team events | 950 jobs 401(k) matching | 864 jobs Conferences | 648 jobs Flexible spending account | 592 jobs Wellness | 535 jobs Home office stipend | 430 jobs Transparency | 290 jobs Relocation support | 242 jobs Unlimited paid time off | 172 jobs

Salary Composition for Machine Learning Engineers in the UK

The salary for a Machine Learning Engineer in the UK typically comprises a base salary, performance bonuses, and additional remuneration such as stock options or benefits. The base salary is the fixed component and usually forms the largest part of the total compensation package. Performance bonuses can vary significantly depending on the company's success and individual performance, often ranging from 10% to 20% of the base salary. Additional remuneration might include stock options, especially in tech companies or startups, and other benefits like health insurance, retirement contributions, and professional development allowances.

The composition can vary based on several factors:

• Region: Salaries in London and other major tech hubs like Cambridge or Manchester tend to be higher due to the cost of living and demand for tech talent.
• Industry: Industries such as finance, healthcare, and technology often offer higher salaries compared to academia or non-profit sectors.
• Company Size: Larger companies or well-funded startups may offer more competitive compensation packages, including significant bonuses and stock options.

Steps to Increase Salary from a Machine Learning Engineer Position

To increase your salary beyond the median level, consider the following strategies:

• Specialize in High-Demand Areas: Focus on niche areas within AI/ML, such as deep learning, natural language processing, or computer vision, which are in high demand.
• Pursue Leadership Roles: Transition into roles with more responsibility, such as a lead engineer or manager, which typically come with higher pay.
• Enhance Your Skills: Continuously update your skills with the latest technologies and methodologies in AI/ML. This could involve learning new programming languages, frameworks, or tools.
• Network and Build a Personal Brand: Attend industry conferences, contribute to open-source projects, and publish research or articles to increase your visibility and reputation in the field.
• Negotiate Effectively: When offered a new position or during performance reviews, negotiate your salary and benefits package assertively.

Educational Requirements for Machine Learning Engineers

Most Machine Learning Engineer positions require at least a bachelor's degree in a related field such as computer science, mathematics, statistics, or engineering. However, a master's degree or Ph.D. is often preferred, especially for roles involving research or advanced algorithm development. These advanced degrees provide a deeper understanding of machine learning theories and practices, which can be crucial for complex problem-solving.

Helpful Certifications for Machine Learning Engineers

While not always required, certain certifications can enhance your credentials and demonstrate your expertise:

• Google Professional Machine Learning Engineer: Validates your ability to design, build, and productionize ML models on Google Cloud.
• AWS Certified Machine Learning – Specialty: Demonstrates your ability to build, train, tune, and deploy ML models on AWS.
• Microsoft Certified: Azure AI Engineer Associate: Focuses on using Azure services to build and deploy AI solutions.

These certifications can be particularly beneficial if you are working with specific cloud platforms or tools.

Experience Required for Machine Learning Engineers

Typically, employers look for candidates with at least 2-5 years of experience in related roles. This experience should include hands-on work with machine learning models, data analysis, and software development. Experience with specific tools and frameworks like TensorFlow, PyTorch, or Scikit-learn is often required. Additionally, experience in deploying models into production and working in cross-functional teams can be highly advantageous.

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