AI Scientist

About the Role

Supports the Biometrics Innovation Team by developing AI solutions for biometric recognition, identity verification, image processing, video analytics, and signal processing applications. Applies machine learning and deep learning technologies across biometric sensing platforms. Collaborates with engineering, product, and innovation teams to develop and deploy production-ready capabilities. Contributes to biometric performance, security, and user experience improvements. Advances HID biometric technology and product differentiation.

Key Responsibilities

• Designing, developing, training, evaluating, and optimizing AI and deep learning models for biometric applications.
• Developing image processing, video analytics, biometric signal processing, feature extraction, and model evaluation pipelines.
• Defining and developing data collection tools, datasets, annotation workflows, and benchmarking processes.
• Applying data engineering, data quality, and data governance practices throughout the AI lifecycle.
• Supporting the deployment and continuous improvement of biometric AI solutions in production environments.
• Supporting AI innovation initiatives and product development activities.
• Supporting cross-functional collaboration with engineering, product management, and business stakeholders.
• Evaluating and supporting the adoption of emerging AI technologies for biometric applications.
• Improving model development, deployment, and evaluation methodologies.
• Supporting biometric application development across multiple sensing platforms.

Requirements & Tech Stack

• Python
• PyTorch or TensorFlow
• OpenCV
• NumPy
• Pandas
• Jupyter
• CNNs
• Vision Transformers (ViT)
• Attention networks
• Hybrid methods
• Image processing
• Video analytics
• Object detection
• Feature extraction
• Image quality assessment

Qualifications

• PhD in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, Signal Processing, Electrical Engineering, or a related technical field.
• 0-4 years of industry, research, or postdoctoral experience.
• Familiarity with face recognition, face matching, presentation attack detection (PAD), liveness detection, identity verification, and biometric recognition systems.
• Exposure to biometric modalities including face, fingerprint, iris, and palm recognition is preferred.
• Exposure to imaging modalities including RGB, NIR, IR, thermal, structured-light, ToF/3D depth, polarization, and multispectral imaging is preferred.
• Knowledge of biometric standards, compliance requirements, and benchmarking methodologies is preferred.
• Experience with MLOps, software engineering, data engineering, and responsible AI governance is preferred.