Biometric AI Scientist - Vision & Identity Solutions

HID Global Corporation

Monet (FL)

On-site

USD 130,000 - 180,000

Full time

14 days+

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Benefits offered by this job

Competitive salary

Benefits and annual leave

Inclusive culture

Career development opportunities

Global organization

Job summary

HID Global Corporation is seeking an AI Scientist to advance biometric AI capabilities in identity technologies. The role involves designing, training, and evaluating AI models for biometric applications, plus building image processing and video analytics pipelines.

You will collaborate with engineering, product, and innovation teams to deploy production-ready solutions across face, fingerprint, iris, and palm modalities, contributing to security and user experience improvements.

Qualifications

PhD in Computer Science, AI, ML, CV, Signal Processing, Electrical Engineering, or related field.
0-4 years of industry, research, or postdoctoral experience.
Experience with Python, PyTorch or TensorFlow, OpenCV, NumPy, Pandas, and Jupyter.
Knowledge of CNNs, Vision Transformers (ViT), attention networks, hybrid methods, image processing, video analytics, object detection, feature extraction, and image quality assessment.
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.

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.