Think biometrics are cool? We make them untouchable. At IDCentriq, we’re not just playing in the security space - we’re rewriting the rules. Our game-changing palm vein tech is setting the gold standard for identity and payment security, and we’re on the hunt for bold thinkers to help us stay ahead of the curve.

We help governments and organizations level up their ID programs with solutions that are sleek, seamless, and seriously secure. Our secret weapon? Proprietary palm vein technology that locks users to an encrypted key pair and an unbreakable chain of custody - meaning tampering and spoofing don’t stand a chance.

Even better, our systems plug effortlessly into existing infrastructure, are low-maintenance, and make life easier for everyone who uses them. If you’re ready to join a team that’s reinventing how the world proves identity, we’re ready for you!

About the Role:

The Senior Computer Vision Engineer will be instrumental in developing next-generation palm print and palm vein biometric authentication systems that surpass current industry standards. This role requires deep expertise in both classical computer vision techniques and modern deep learning approaches, with particular emphasis on biometric image processing, anti-spoofing technologies, and real-world deployment constraints.

The ideal candidate has proven experience taking computer vision systems from research through production deployment, with specific expertise in biometric or identity verification applications, particularly palm-based systems. You must be comfortable working with hardware constraints, optimizing for edge deployment, and collaborating closely with ML engineers to build end-to-end biometric pipelines. This role demands someone who can balance theoretical knowledge with practical implementation skills, delivering robust solutions that work reliably across diverse populations and environmental conditions.

Responsibilities:

Design and implement advanced computer vision algorithms specifically for palm print and palm vein biometric authentication systems.
Develop state-of-the-art anti-spoofing and presentation attack detection (PAD) systems to defeat biometric fraud attempt.
Build image preprocessing pipelines optimized for biometric feature preservation while handling real-world capture variations.
Optimize biometric image quality assessment providing real-time feedback to ensure captured data meets authentication thresholds.
Work directly with biometric sensors (high-resolution visible light and specialized NIR cameras) tuning capture parameters for optimal feature extraction.
Deploy optimized biometric algorithms to edge devices achieving real-time authentication performance.
Support development of augmentation strategies simulating demographic variations in palm presentation to ensure cross-population fairness.
Create calibration procedures for multi-modal biometric systems ensuring consistent quality across diverse hardware.
Conduct systematic failure analysis on biometric system performance to identify and resolve issues related to demographics, environmental conditions, or user behavior.
Collaborate with ML engineers to maximize discriminative power of biometric features for neural network training .Build automated testing frameworks validating biometric performance across compliance benchmarks.
Collaborate with hardware vendors defining next-generation biometric sensor specifications and requirements.

Desired Skills and Experience:

5+ years production experience in computer vision with minimum 3 years in biometric systems or identity verification.
Proven expertise in biometric anti-spoofing, liveness detection, and presentation attack detection.
Deep experience with fingerprint, face, iris, or palm biometric systems including template extraction and matching, with strong preference for palm biometrics expertise.
Strong background in biometric image quality assessment and enhancement techniques.
Hands-on experience with biometric sensors, NIR imaging, multispectral capture, or 3D depth sensing.
Demonstrated ability to optimize biometric algorithms for edge deployment achieving real-time performance.
Expertise in classical biometric techniques (minutiae extraction, ridge flow, texture analysis) and modern deep learning approaches, with preference for palm biometric experience.
Experience with biometric template protection, cancelable biometrics, and privacy-preserving schemes.
Strong knowledge of biometric standards and performance metrics.
Proficiency in Python and C++ with experience in OpenCV, biometric SDKs, and embedded deployment.
Background in demographic bias mitigation and cross-population performance optimization for biometrics.
Experience with related fields valuable: medical imaging, forensic analysis, or high-security authentication systems.
Published research or patents in biometric recognition, computer vision, or pattern recognition preferred.
Bachelor's degree in Computer Science, Electrical Engineering, or related field; MS/PhD in biometrics or computer vision strongly preferred.

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