1 month ago
Remote, IndiaStaff+
Responsibilities
- Lead the design and development of computer vision systems for biometric face attributes, detection, quality assessment, and recognition.
- Conduct fairness analysis and benchmarking across datasets and operating conditions, and measure and mitigate disparate impact.
- 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 automated Airflow pipelines 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.
- Scale training jobs on multi-GPU clusters and deploy production services.
- Mentor ML engineers, conduct code and design reviews, and drive technical 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 and biometrics, especially face recognition.
- Practical experience identifying, measuring, and mitigating algorithmic bias and disparate impact in computer vision.
- Expert Python proficiency with machine learning and vision libraries such as Pillow, OpenCV, and PyTorch, and experience writing production-ready code.
- Experience designing end-to-end ML pipelines and working with workflow orchestrators such as Airflow.
- Hands-on experience scaling training on multi-GPU clusters and deploying services on AWS, including SageMaker, EC2, and EKS.
- Preferred experience with research publications in CVPR, ICCV, ECCV, or FG; vector databases such as Milvus or Faiss; approximate nearest neighbor search; biometric privacy, security, and compliance; CoreML, LiteRT, or TFLite; GANs or diffusion models; and synthetic face generation.
- Strong communication skills.
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