About the job :

Roles and Responsibility :

- 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 optimize 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.

- Optimize models for low-latency inference (quantization, 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.

What Were Looking For :

- Experience : 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 & 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)