Live opening · Posted 8 days ago
The key details from the original listing.
Live timing from JobBeeper.
Description supplied by the original job listing.
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.
Experience
6-10 yrs
Recently tracked roles with full details and direct application links.