Computer Vision ML Engineer - Edge Deployment & Vision Nav

Lindus Health Limited; Greater London; Hybrid; GBP 70,000 - 120,000; 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 seeks an experienced machine learning engineer to design, train and deploy vision-based models for autonomous vehicles. You will collect datasets, develop deep neural networks, and optimize inference on edge hardware while coordinating with flight control engineers to ensure reliable guidance.

Strong Python and PyTorch skills are essential, with experience deploying models to embedded systems and real-world hardware. Hybrid London-based work is expected.

Qualifications

• Hands-on experience training and evaluating deep neural networks on real-world image/video data.
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• Strong Python and PyTorch experience.
• Ability to deploy models to embedded hardware and on-edge compute.

Responsibilities

• Build and improve the models behind vision-based navigation, perception and guidance.
• Design and collect annotation pipelines for real and simulated datasets.
• Design and train deep neural network based computer vision models.
• Optimise models for deployment on edge compute.
• Collaborate with flight control engineers to integrate model outputs into guidance.
• Diagnose failure modes from flight data and fix via data or model changes.

Skills

Python

Deep Learning

Edge Deployment

Tools

PyTorch

Model deployment on embedded hardware

Job description

Minerva Defence seeks an experienced machine learning engineer to design, train and deploy vision-based models for autonomous vehicles. You will collect datasets, develop deep neural networks, and optimize inference on edge hardware while coordinating with flight control engineers to ensure reliable guidance.

Strong Python and PyTorch skills are essential, with experience deploying models to embedded systems and real-world hardware. Hybrid London-based work is expected.