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Staff AI Engineer – Computer Vision & ML

Brio Digital
London
2 months ago
Applications closed

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Staff Engineer – Computer Vision & ML

Hybrid (2+ days/week in London)

Permanent | up to £160k + equity


A well-funded startup is redefining how the world's top brands understand and act on real-world data. Their product uses state-of-the-art Visual AI to deliver insights in retail environments—at scale, in real time, and on edge devices.


As they continue to scale across Europe and the US, they are hiring aStaff Engineerto lead technical direction across their Machine Learning and Computer Vision teams. This is a hands-on leadership role suited to someone who thrives in applied AI environments and knows how to balance architectural vision with practical execution.


What You’ll Do

  • Lead the technical direction for applied ML/CV efforts across edge and mobile platforms
  • Architect and optimise scalable vision pipelines for real-world performance
  • Act as a mentor and multiplier—raising the bar across a team of ML/CV engineers
  • Stay close to code: from rapid prototyping to production-ready models
  • Evaluate, test and deploy new techniques (e.g. synthetic data, efficient fine-tuning methods, robust inference strategies)
  • Collaborate cross-functionally with product, infra, and customer success teams


What We’re Looking For

  • Proven track record delivering applied ML/CV solutions as an individual contributor
  • Deep experience with detection/recognition models (e.g. YOLO, Mask R-CNN, custom pipelines)
  • Practical understanding of edge deployment constraints (latency, performance, robustness)
  • Strong Python skills and familiarity with libraries like PyTorch, OpenCV, and TensorRT
  • Experience leading technical direction and mentoring other engineers
  • Ability to own problems end-to-end, with minimal external support


Bonus Points For

  • Experience with synthetic data generation and domain adaptation techniques
  • Contributions to open-source ML/CV projects
  • Experience working with mobile ML frameworks (e.g. Core ML, ONNX, TFLite)

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