Machine Learning Systems Engineer

Minimum:

• Experience with machine learning pipelines, from datasets through to evaluation and deployment (ML Ops)
• Experience with Python
• A solid grounding in backend development
• Experience with handling large datasets (computer vision or otherwise)

Preferred:

• Experience with deep neural nets
• Experience with computer vision
• Experience deploying ML models on edge
• Interest in working with unmanned aircraft

As an ML systems engineer with Minerva you will work on the infrastructure and systems required for improving the performance of our machine learning models deployed across our entire fleet of vehicles in operational environments.

Minerva Defence is one of the fastest-growing defence technology companies in Europe. We create the software, hardware and networking that make unmanned systems effective on the modern battlefield, all ideated, designed and manufactured end-to-end in the UK, and in active use today. We are deliberately low-profile: a team of around 70, small in headcount and outsized in effect. We look for people who are not just talented but genuinely committed to the work, the team, and the purpose behind it.

You will work on the infrastructure required to improve our machine learning models running on large quantities of deployed vehicles. You could be responsible for the collection of training data (building the pipelines to securely return data from operational platforms), generation of synthetic data, annotation, training infrastructure, evaluation, optimisation of inference on edge devices or deployment. In many cases, data will comprise of large dumps of images or videos, and you should be comfortable architecting systems to deal with this. We are looking for candidates who can work across the full stack, and who are willing to jump into learning about new areas they are less familiar with if that’s what is needed to get the job done.

Responsibilities

• Build out the infrastructure required to improve the performance of our machine learning models, including dataset curation/labelling, training, evaluation and deployment.
• Ensure model performance is constantly improving, whether that involves building the pipelines to collect operational data, supervising annotation/labelling, generating synthetic data, training models or optimising inference.

We offer competitive compensation including;

• Share options vesting over three years
• Bupa private medical insurance
• 6% employer pension contribution
• 25 days leave plus bank holidays
• Enhanced mat/pat leave
• Enterprise level AI tooling
• Relocation support/home office budget