Applied machine learning engineer job description.

What does an applied machine learning engineer do?

 

An applied machine learning engineer focuses on solving specific business problems using machine learning models. Their work is grounded in real-world impact, often optimising recommendations, forecasts, or user behaviour predictions.

 

Key tasks include collaborating with domain experts, transforming business challenges into ML solutions, and deploying models that deliver measurable value. They often use platforms like SageMaker, Vertex AI, or Databricks alongside open-source tools.

 

In lean teams, they may work across feature engineering and model training. In enterprise settings, they embed within product squads, rapidly iterating and aligning outputs with KPIs such as retention, conversion, or fraud detection.

 

Key responsibilities of an applied machine learning engineer.

 

Applied ML engineers focus on using models to solve real-world business problems. Their responsibilities include:

• Translating business requirements into ML projects and model pipelines

• Working with domain experts to scope problems and define success criteria

• Developing, training, and evaluating supervised or unsupervised models

• Deploying models to production and monitoring performance over time

• Supporting personalisation, prediction, recommendation, or classification use cases

• Communicating findings and results to stakeholders in non-technical terms

• Collaborating with data and engineering teams on integration

• Running A/B tests and model validation experiments

• Maintaining documentation and model lifecycle records

• Tracking regulatory or ethical constraints for applied models

This role blends practical model application with business impact and usability.

 

Skills and requirements for an applied machine learning engineer.

 

Applied ML engineers translate research into practical business value. Employers typically look for:

• 3–6 years of experience in applied ML or data science

• Experience adapting and tuning open-source or pre-trained models

• Strong coding skills and understanding of ML production environments

• Familiarity with data wrangling, feature engineering, and model validation

• Ability to balance accuracy, interpretability, and business objectives

• Skilled in collaborating with product, design, and data teams

• Experience validating models, reducing bias or drift

• Understanding ML integration into user-facing systems

• Comfortable working across various data types (structured, unstructured)

Most Applied ML engineers deploy and monitor business-focused models.

 

Average salary for an applied machine learning engineer.

 

In the UK, the average salary for an applied machine learning engineer typically ranges from £50,000 to £85,000, reflecting practical deployment of AI and data models.

• Mid-level engineers earn between £50,000 and £67,000

• Senior professionals deploying models in real-world product contexts may earn between £68,000 and £85,000

• Knowledge of real-time data processing and scalable systems adds salary value

Top-paying companies are in tech platforms, ecommerce, and AI product development.

 

Career progression for an applied machine learning engineer.

 

An applied ML engineer focuses on solving real-world business problems through data-driven models and experimentation. This role has strong progression into product-focused or leadership positions. A common path includes:

 

Data analyst / ML associate

 

Runs experiments, prepares data pipelines, and supports exploratory model development.

 

Applied ML engineer

 

Deploys models in real-world environments. Works closely with product, data, and engineering teams.

 

Senior applied ML engineer

 

Optimises models for business KPIs such as retention, revenue, or conversions.

 

ML lead

 

Owns model strategy for a product area. Collaborates on feature design and user impact.

 

Head of ML / Director of AI

 

Shapes the broader machine learning roadmap and team capability across the organisation.