2 months ago
Remote, IndiaStaff+
Responsibilities
- Lead the design and development of computer vision and biometric systems for face attributes, detection, quality, and recognition.
- Conduct fairness analysis and benchmarking of biometric models across datasets and operating conditions.
- Architect, train, and optimize models using PyTorch, TensorFlow, and/or JAX.
- Own end-to-end ML pipelines from data ingestion through training and deployment, including Airflow automation for ingestion and cleaning.
- Curate balanced training datasets and generate synthetic data to address quality and diversity gaps.
- Optimize models for low-latency inference using quantization, distillation, TensorRT, and ONNX, and manage deployment on AWS.
- Mentor ML engineers, conduct code and design reviews, and drive technical best practices across the Computer Vision team.
Requirements
- 5+ years of industry experience in machine learning, including at least 3 years dedicated to biometrics or face analysis.
- Deep expertise in computer vision, biometrics, and especially face recognition.
- Practical experience measuring and mitigating algorithmic bias and disparate impact in computer vision.
- Expert proficiency in Python and machine learning or vision libraries such as Pillow, OpenCV, and PyTorch.
- Experience designing end-to-end ML pipelines and working with workflow orchestrators such as Airflow.
- Hands-on experience scaling training jobs on multi-GPU clusters and deploying services on AWS, including SageMaker, EC2, and EKS.
- Preferred qualifications include research publications in CVPR, ICCV, ECCV, or FG; experience with Milvus, Faiss, and approximate nearest-neighbor search; familiarity with privacy, security, and compliance in biometric systems; mobile or edge model deployment using CoreML, LiteRT, or TFLite; synthetic face generation using GANs or diffusion models; and strong communication skills.
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