Machine Learning Engineer

Xcede; Greater London; On-site; GBP 70,000 - 100,000; Full time

Job summary

Xcede is partnering with a fast-growing logistics and e-commerce tech company in London to recruit an ML Engineer focused on the engineering side of ML. You will drive models from development to live deployment, monitor real-world performance, and help scale the ML platform as the product handles higher parcel volumes.

The role emphasizes building shared tools, defining best practices, and ensuring reliability and speed at scale.

Qualifications

• Solid background in software engineering.
• Track record of getting ML models into production.
• Understanding of how to test, monitor and maintain ML systems.

Responsibilities

• Take models from training through live deployment.
• Track model performance and business value in production.
• Shape and grow the ML platform and how models are served.
• Create shared tools and define ML best practices across the company.
• Ensure models run quickly, reliably and at scale.
• Set up testing, automated release processes and monitoring for drift.
• Partner with data scientists to roll out new processes and assess results.

Skills

Software engineering

ML deployment

Testing/monitoring ML systems

Job description

Xcede have started working with a fast-growing logistics and e-commerce tech company on a mission to make online delivery faster, cheaper and more efficient.

AI isn’t just on their roadmap, it’s already at the heart of the product! Machine learning touches every single parcel.

Their growth has been huge, going from around 30K parcels a day to an average of 200K a day in just over a year. This means you will be joining at a time where you can have a real, measurable impact on a product used at serious scale.

Their ideal candidate will have a strong software engineering background and now be working as an ML Engineer focused on the engineering side of ML, rather than modelling or data science.

You will:

• Take models from the training stage all the way through to live deployment
• Track how models perform in the real world and the value they deliver to the business
• Shape and grow the ML platform, including how models are served
• Create shared tools and define best practice for ML across the company
• Make sure models run quickly, reliably and at scale
• Set up testing, automated release processes and monitoring to catch model drift
• Partner with data scientists to roll out new processes and assess the results

Requirements:

• Solid background in software engineering
• A track record of getting ML models into production
• Understanding of how to test, monitor and maintain ML systems