Machine Learning Engineer

Platform Recruitment; Greater London; On-site; GBP 42,000 - 70,000; Full time

Job summary

Platform Recruitment in London is seeking a Machine Learning Engineer to design, build, and deploy advanced ML models that solve complex operational challenges in real-world environments. You will work with time-series data, forecasting, optimisation, and probabilistic modelling to create scalable systems in collaboration with engineering and product teams.

This hands-on role partners with a high-calibre founding team to move models from early validation to production deployment, handling

Qualifications

• Strong academic background in a quantitative STEM discipline such as Computer Science, Mathematics, Physics, Engineering, or related fields.
• Commercial experience developing machine learning solutions using Python and modern ML frameworks
• Strong understanding of applied machine learning techniques, particularly around modelling complex systems and large-scale datasets
• Experience working with time-series, forecasting, optimisation, or probabilistic modelling approaches
• Comfortable working with unstructured or inconsistent real-world data in production settings
• A problem-solving mindset with the ability to work autonomously within a fast-moving environment
• Interest in applying machine learning to operational, industrial, or physical systems

Responsibilities

• design, build, and deploy advanced machine learning models capable of solving complex operational challenges in real-world environments.
• Working closely with engineering and product teams, you will help develop scalable systems that can operate effectively with imperfect, high-volume data while contributing to the long-term technical direction of the platform.

Skills

Python

ML frameworks

Time-series

Forecasting

Optimization

Probabilistic modelling

Production data handling

Education

Quantitative STEM degree

Job description

Job Title: Machine Learning Engineer

Location: London

Salary: Up to £70,000 DOE

This is a unique opportunity for a Machine Learning Engineer to join a well‑funded early‑stage company building frontier AI systems focused on optimising complex operational environments. Working at the intersection of scientific machine learning, time‑series modelling, optimisation, and real‑world deployment, you will help shape products designed for measurable real‑world impact.

This is a hands‑on engineering role where you will work closely with a high‑calibre founding team, industrial data, and customer environments to take machine learning systems from early validation through to scalable deployment.

The Role

You will design, build, and deploy advanced machine learning models capable of solving complex operational challenges in real‑world environments. Working closely with engineering and product teams, you will help develop scalable systems that can operate effectively with imperfect, high‑volume data while contributing to the long‑term technical direction of the platform.

Requirements

• A strong academic background in a quantitative STEM discipline such as Computer Science, Mathematics, Physics, Engineering, or related fields
• Commercial experience developing machine learning solutions using Python and modern ML frameworks
• Strong understanding of applied machine learning techniques, particularly around modelling complex systems and large‑scale datasets
• Experience working with time‑series, forecasting, optimisation, or probabilistic modelling approaches
• Comfortable working with unstructured or inconsistent real‑world data in production settings
• A problem‑solving mindset with the ability to work autonomously within a fast‑moving environment
• Interest in applying machine learning to operational, industrial, or physical systems

Desirable Experience

• Experience deploying ML systems into production environments
• Exposure to scientific machine learning, simulation, or reinforcement learning
• Familiarity with cloud infrastructure and scalable ML pipelines

If this opportunity is of interest, please apply below