Location
London

Hours
Full Time

Salary
Competitive, based on experience

About the Role
As a Machine Learning Systems Engineer at Minerva Defence, you will develop and maintain the infrastructure and systems that enhance the performance of machine learning models deployed across our entire fleet of operational vehicles. Minerva Defence is one of Europe's fastest-growing defence technology companies, designing and manufacturing software, hardware, and networking solutions for unmanned systems used on the modern battlefield. Our team is small but highly impactful, committed to delivering cutting-edge technology with a strong sense of purpose.

You will be responsible for building and improving pipelines for collecting training data securely from operational platforms, generating synthetic data, annotation, training infrastructure, evaluation, optimization of inference on edge devices, and deployment. The role requires working with large datasets, often comprising images or videos, and architecting robust systems to handle them efficiently. We seek candidates who can work across the full stack and are eager to learn new skills as needed to achieve project goals.

Experience
- Experience with machine learning pipelines, including dataset curation, evaluation, and deployment (ML Ops)
- Proficiency in Python
- Strong backend development skills
- Experience handling large datasets, particularly in computer vision or related fields

Preferred
- Experience with deep neural networks
- Experience with computer vision applications
- Experience deploying ML models on edge devices
- Interest in working with unmanned aircraft

About you
You are a motivated engineer who embraces challenges and is willing to learn new areas to get the job done. You thrive in a collaborative, mission-driven environment and are passionate about advancing defence technology through innovative machine learning solutions.

Qualifications
Relevant degree or equivalent experience in computer science, engineering, or a related field is desirable but not mandatory if you demonstrate strong practical skills and commitment.