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We're looking for a Senior Machine Learning Engineer with strong computer vision expertise to join our Biometrics team. You'll ramp up on the biometrics domain while contributing to the design, training, and scaling of our ML systems end-to-end on AWS. The final job level for this role will be determined following the interview process.

• Design and develop computer vision models and pipelines, contributing to biometrics use cases such as face detection, quality assessment, and recognition
• Collaborate on benchmarking of models across datasets and operating conditions
• Train and optimize models using PyTorch, TensorFlow, and/or JAX
• Contribute to end-to-end ML pipelines, from data ingestion to deployment. Help design automated pipelines (Airflow) for data ingestion and cleaning.
• Production Engineering: Optimize models for low-latency inference (quantization, distillation, TensorRT/ONNX) and support deployment on AWS.
• Collaborate with and mentor ML engineers, and contribute to technical best practices across the Computer Vision team.

• Experience: 5+ years of industry experience in Machine Learning with a focus on Computer Vision (e.g., image classification, object detection, segmentation, image quality, generative models)
• Strong Computer Vision fundamentals
• Fairness & Ethics: Awareness of algorithmic bias and willingness to learn domain-specific fairness practices
• Strong Engineering: Strong proficiency in Python (Pillow, OpenCV, PyTorch, etc). You write clean, modular, production-ready code
• Systems Architecture: Experience designing or contributing to ML pipelines and familiarity with orchestration tools like Airflow
• Cloud Native: Experience with GPU-based training and deploying ML services on AWS

• Research Publications in top computer vision venues (CVPR, ICCV, ECCV)
• Large Scale Search: Familiarity with vector databases and ANN search
• Synthetic Data: Experience with GANs or diffusion models for data augmentation
• Mobile/Edge Experience: CoreML, LiteRT, and/or TFLite
• Familiarity with privacy, security, and compliance considerations in sensitive ML applications