Edge Vision ML Engineer for On-Edge Navigation

Minerva Defence; Worcester; Hybrid; GBP 85,000 - 125,000; Full time

Benefits offered by this job

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

Job summary

Minerva Defence is seeking a Machine Learning Engineer to design, train, evaluate and deploy ML models for vision and autonomy on a fleet of unmanned vehicles. You’ll work in the computer vision team across data collection, model selection, and edge-optimised deployment, collaborating with flight control engineers to ensure reliable guidance.

The role requires hands-on DL experience, strong Python skills, and practical deployment expertise, with the London office operating on a hybrid basis.

Qualifications

• Hands-on experience training and evaluating deep neural networks on real-world image or video data.
• Strong Python, plus experience with a major deep learning framework such as PyTorch.
• Experience getting a model running on real hardware or in a production system.
• Able to work from our London office on a hybrid basis.

Responsibilities

• Build and improve the models behind vision-based navigation, perception and guidance.
• Design, collect and if necessary build annotation pipelines for real and simulated datasets.
• Design and train deep neural network based computer vision models
• Optimise models for deployment on edge compute
• Work with the flight control engineers so model outputs feed reliably into guidance
• Diagnose failure modes from flight data and fix them through data or model changes

Skills

Deep neural networks

Python

PyTorch

Edge deployment

Job description

Minerva Defence is seeking a Machine Learning Engineer to design, train, evaluate and deploy ML models for vision and autonomy on a fleet of unmanned vehicles. You’ll work in the computer vision team across data collection, model selection, and edge-optimised deployment, collaborating with flight control engineers to ensure reliable guidance.

The role requires hands-on DL experience, strong Python skills, and practical deployment expertise, with the London office operating on a hybrid basis.