Sr. Machine Learning Engineer- Computer Vision (Liveness Detection & Spoofing) in United States at Jobgether

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Sr. Machine Learning Engineer – Computer Vision (Liveness Detection & Spoofing) in United States.

This role is focused on building advanced computer vision and machine learning systems that power next-generation identity verification and fraud prevention technologies. You will work on highly impactful problems such as face liveness detection, presentation attack detection, deepfake recognition, and biometric authentication in adversarial environments. The position spans the full ML lifecycle, from data engineering and model development to production deployment and monitoring at scale. You will contribute to systems that must be both highly accurate and resilient against evolving fraud techniques. Working in a collaborative, engineering-driven environment, you will partner with product, fraud, and platform teams to ensure robust, compliant, and scalable AI solutions. This is a hands-on role where innovation, experimentation, and production excellence directly strengthen global digital identity security.

• Design, build, train, and optimize computer vision and deep learning models for image classification, face liveness detection, and anti-spoofing (PAD) systems.
• Develop robust ML solutions for identity verification challenges, including deepfakes, replay attacks, synthetic media, and other adversarial threats.
• Build end-to-end ML pipelines covering data ingestion, preprocessing, labeling, augmentation, training, evaluation, and production deployment.
• Define and implement evaluation metrics balancing fraud detection performance, false acceptance/rejection rates, and real-world business impact.
• Conduct experimentation using techniques such as architecture tuning, hard-negative mining, and data balancing to improve model robustness and accuracy.
• Deploy and maintain production-grade ML services on cloud infrastructure (AWS), ensuring scalability, reliability, and observability.
• Collaborate with cross-functional teams (Product, Fraud, Engineering, Platform) to align ML solutions with security, compliance, and business requirements.
• Research and integrate emerging advances in computer vision, biometric authentication, and adversarial ML to strengthen fraud detection systems.
• Support code reviews, model reviews, and knowledge sharing to elevate engineering standards across the team.

• Bachelor’s degree in Computer Science, Engineering, or related technical field, or equivalent practical experience.
• 5+ years of experience in machine learning engineering or applied computer vision roles.
• Strong Python programming skills with experience building production-grade ML systems.
• Hands-on expertise with deep learning frameworks such as PyTorch or TensorFlow.
• Strong background in computer vision, including CNNs, Vision Transformers, image processing, and feature extraction.
• Experience working with large-scale image datasets, including data cleaning, labeling strategies, augmentation, and dataset QA.
• Proven ability to deploy ML models into production and build scalable training and inference pipelines.
• Strong understanding of model performance tradeoffs (precision, recall, false positives/negatives, robustness).
• Experience working in noisy, imbalanced, or adversarial data environments.
• Excellent communication and collaboration skills across technical and non-technical stakeholders.
• Experience in biometric authentication, liveness detection, or fraud detection systems is highly preferred.

• Competitive salary range of $150,000–$185,000 annually, plus up to 10% annual bonus eligibility.
• Comprehensive health coverage (medical, dental, vision) and wellness support.
• Retirement and financial programs, including stock participation and pension/401(k)-style plans.
• Flexible paid time off, company holidays, and volunteer days.
• Remote-first flexibility with optional office access depending on preference.
• Learning and development support, including training programs, tuition reimbursement, and hackathons.
• Home office setup allowance and modern engineering tooling.
• Additional optional benefits such as legal assistance, identity protection, and pet insurance.
• Inclusive, innovation-driven culture focused on meaningful, mission-critical AI work.