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Senior Computer Vision/ Machine Learning Engineer
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Senior Machine Learning Engineer - Computer Vision
Location: UK
We're looking for a Senior Machine Learning Engineer with strong Computer Vision and Physical Face Recognition experience to take ownership of a complex capability from research through to real-world deployment.
This is a genuinely hands-on role. You'll work across the full lifecycle of a production face recognition system - developing models, building evaluation frameworks, and optimizing deployment on edge GPU hardware.
This isn't a research-only role. You'll be taking models from experimentation through to live, production systems and solving the real-world challenges that come with it.
About the Role:
You'll take ownership of the face recognition pipeline, including:
- Physical Face detection, alignment, quality filtering, and embedding extraction.
- Training and fine-tuning face recognition models.
- Building robust 1:1 verification and 1:N identification evaluation frameworks.
- Developing multi-camera Computer Vision pipelines.
- Optimizing models for edge deployment using ONNX and TensorRT, including FP16/INT8 quantization.
- Balancing accuracy, latency, throughput, and GPU compute constraints.
- Designing matching, gallery, and enrolment infrastructure.
- Benchmarking model performance and tracking improvements across versions.
- Reviewing code and mentoring other engineers.
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What We're Looking For:
Face Recognition - Essential
You must have hands-on experience building and deploying production face recognition systems.
Computer Vision
- Learned embeddings and open-set matching.
- Modern face recognition approaches such as RetinaFace, SCRFD, ArcFace, CosFace, or AdaFace.
- Face detection, landmark alignment, and embedding models.
- 1:1 verification and 1:N identification.
- Evaluating biometric systems using metrics such as TAR/FAR, FMR/FNMR, DET curves, and Rank-N accuracy.
- Evaluating model performance in challenging, real-world environments rather than relying solely on standard benchmarks.
Technical Experience
- Strong Python and PyTorch experience.
- Production Computer Vision and Machine Learning experience.
- ONNX and TensorRT deployment.
- NVIDIA GPU inference and optimization.
- Understanding of batching, memory constraints, latency, and throughput.
- Linux, Docker, Git, and CI.
- C++ experience would be beneficial.
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Highly Desirable
- NVIDIA DeepStream, GStreamer, or Triton.
- Face recognition in challenging environments, including low-resolution imagery, motion blur, occlusion, and difficult lighting.
- ANN/vector search, including FAISS or HNSW.
- Multi-camera tracking or identity association.
- Face quality assessment or template fusion.
- Liveness or presentation attack detection.
- Model optimization, distillation, or pruning.
- Synthetic or augmented training data.
The Person
We're looking for someone with strong engineering judgment who can confidently answer a simple but important question: is this model ready for production?
You'll be comfortable working independently, making technical decisions based on data and performance, and challenging assumptions when something isn't good enough.
You'll also have the opportunity to help shape how complex Computer Vision systems are built, evaluated, and deployed in real-world, performance-constrained environments.
If you have genuine production Face Recognition experience and enjoy solving difficult Computer Vision problems beyond the research stage, this is an opportunity to have real technical ownership and impact.
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