Computer Vision Engineer

Responsibilities:

• Lead the design and development of computer vision systems for biometrics (face attributes, detection, quality, and recognition).
• Rigorous fairness analysis and benchmarking of biometric models across various datasets and operating conditions.
• Architect, train, and optimise models using PyTorch, Tensorflow, and/or JAX.
• Own and evolve end-to-end ML pipelines, from data ingestion to deployment.
• Design automated pipelines (Airflow) for data ingestion and cleaning.
• You will be responsible for curating balanced training sets and generating synthetic data to address both quality and diversity gaps.
• Production Engineering: Own the path to production.
• Optimise models for low-latency inference (quantisation, distillation, TensorRT/ONNX) and manage deployment on AWS.
• Mentor ML engineers, conduct code/design reviews, and drive technical best practices across the Computer Vision team.

Requirements:

• 5+ years of industry experience in Machine Learning, with at least 3 years dedicated to Biometrics or Face Analysis.
• Deep expertise in computer vision and biometrics, especially face recognition.

Fairness and Ethics:

• You understand the sources of algorithmic bias in Computer Vision and have practical experience measuring and mitigating disparate impact.
• Strong Engineering: Expert proficiency in Python (both machine learning and vision libraries such as Pillow, OpenCV, PyTorch, etc). You write clean, modular, production-ready code.
• Systems Architecture: Experience designing end-to-end ML pipelines (Data to Train to Deploy) and working with workflow orchestrators like Airflow.

Cloud Native:

• Hands-on experience scaling training jobs on multi-GPU clusters and deploying services on AWS (SageMaker, EC2 EKS).

Nice to Have:

• Research Publications: Papers in CVPR, ICCV, ECCV, or FG related to face recognition, image quality assessment, or fairness.
• Large Scale Search: Experience with vector databases (e. g., Milvus, Faiss) and approximate nearest neighbour (ANN) search algorithms. Familiarity with privacy, security, and compliance in biometric systems.
• Mobile/Edge Experience: Experience porting models to edge or mobile devices utilising frameworks such as CoreML, LiteRT, and/or TFLite.
• Synthetic Data: Experience using GANs or diffusion models to generate synthetic faces for training. Strong communication skills.