Machine Learning Engineer

Role overview Develop and deploy machine learning models that support document verification, facial recognition, liveness detection, and fraud prevention. The work combines applied computer vision and model development with production deployment and ongoing evaluation; the team also contributes to research and industry collaboration.

Responsibilities - Design, train, and optimize models for identity verification use cases. - Develop computer vision methods for document authenticity, facial liveness, and fraud detection. - Work with engineering colleagues to integrate models into production systems. - Track model performance and refine approaches as threats and requirements evolve. - Explore techniques such as document forgery detection, cross-document extraction, behavioral biometrics, and privacy-preserving learning. - Contribute to research sharing, open-source work, or industry standards activities as appropriate.

Requirements - A master’s or doctoral degree in computer science, machine learning, or a related field. - At least three years of machine learning or computer vision experience. - Proficiency in Python and a deep learning framework such as TensorFlow or PyTorch. - Experience with computer vision and OCR technologies. - Understanding of identity verification challenges and experience deploying models in production. - Strong problem-solving skills and attention to detail.

Nice to have - Experience with facial recognition, document forensics, or fraud detection. - Familiarity with deploying models at the edge or with MLOps practices. - Relevant publications in machine learning or computer vision.

Benefits and work setup - Remote role with flexible hours and salary and equity package. - Health, dental, and vision coverage, plus an annual conference and learning stipend. - Access to GPU resources for experimentation.