Biometric Algorithm & SDK Engineer (Contactless Fingerprint Recognition)

About the Role
We are looking for an expert in biometric recognition and computer vision to help us build a proprietary contactless fingerprint recognition SDK. The core objective is to turn raw images of a hand/finger captured by a smartphone camera into standards-compliant fingerprint templates that can be submitted to government or enterprise APIs for verification. This is not about integrating an existing SDK — we need someone who can architect and deliver the underlying technology.

Responsibilities

• Fingerprint Template Generation: Develop algorithms to extract usable fingerprint templates from camera-based fingerphotos.

• Image Processing Pipeline: Implement preprocessing (segmentation, enhancement, normalization) to make captures usable across lighting, angles, and devices.

• Liveness Detection: Integrate Presentation Attack Detection (PAD) to prevent spoofing, aligned with ISO/IEC 30107-3.

• Standards Compliance: Ensure outputs meet ISO/IEC 19794-2, ANSI/NIST ITL, WSQ formats so they can be recognized by government/enterprise systems.

• SDK/API Delivery: Package the solution into a developer-friendly SDK or API with full documentation, sample code, and integration support.

• Performance & Benchmarking: Test against NIST/NFIQ 2 quality scores and benchmark accuracy, latency, and robustness in real-world conditions.

• Integration Support: Work with mobile developers to integrate into iOS/Android apps and with backend teams for API submission.

Qualifications

• Proven experience building fingerprint or palm recognition algorithms (minutiae extraction, ridge analysis, template generation).

• Strong background in computer vision and image processing (segmentation, denoising, feature extraction).

• Hands-on with OpenCV, TensorFlow, PyTorch, CUDA, and mobile deployment (TensorFlow Lite, Core ML).

• Familiar with biometric standards: ISO/IEC 19794-2, ANSI/NIST ITL, WSQ compression, NFIQ 2 image quality.

• Knowledge of liveness detection / PAD (ISO/IEC 30107-3).

• Track record of building SDKs/APIs with developer documentation and integration support.

• Experience working with secure systems (root of trust, data encryption, secure provisioning) is a plus.

• Advanced degree (MS/PhD) in Computer Vision, Machine Learning, or related field preferred.

What Success Looks Like

• A working SDK/API that can take a smartphone-captured fingerphoto and generate a standards-compliant fingerprint template.

• Templates are accepted by a government verification API and pass quality scoring (NFIQ 2).

• The pipeline is robust to real-world conditions (different devices, lighting, angles).

• Liveness detection is integrated and resilient against common spoofing methods.

• Deliverables include SDK/API, documentation, sample integration code, and performance benchmarks.