Machine Learning Engineer
Incode Technologies is an AI-powered identity verification and fraud prevention company. It provides identity, biometric, liveness, KYC/KYB, authentication, age assurance, and deepfake-defense solutions for global enterprises, including financial institutions, fintechs, marketplaces, governments, telcos, healthcare organizations, and gaming companies.
Maintainer signals as of 8/23/2026
Funding history
About Incode Technologies, Inc.
Incode develops and operates an identity platform that verifies users, businesses, and AI agents through document verification, biometrics, facial recognition, liveness detection, deepfake detection, government-record matching, sanctions and PEP screening, and risk decisioning. Its platform includes configurable verification workflows, case management, analytics, integrations, and developer SDKs. The company serves enterprise customers across financial services, fintech, crypto, commerce, marketplaces, public sector, healthcare, travel, telecommunications, gaming, and other industries.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
Design, build, and deploy deep learning models for computer vision applications including facial recognition, liveness detection, and document processing. Architect scalable ML pipelines, optimize models for performance and cost, conduct research, improve data workflows, collaborate cross-functionally, and mentor engineers.
Requirements
- 5+ years of industrial experience in Machine Learning, Deep Learning, or Computer Vision.
- Expertise in Python and deep learning frameworks such as PyTorch or TensorFlow.
- Experience developing, deploying, and optimizing production models for performance and cost efficiency, including edge AI.
- Specialized expertise in facial recognition, liveness detection, document processing, or ID verification.
- Ability to design and maintain end-to-end ML pipelines.
- Research contributions or publications in deep learning and computer vision.
- Excellent communication and cross-functional collaboration skills.
- Passion for mentoring and working across research and rapid iteration cycles.
Responsibilities
- Develop and refine deep learning models for facial recognition, liveness detection, and document processing.
- Architect and maintain scalable ML pipelines and ensure production readiness and cost efficiency.
- Research emerging architectures and translate research into production-grade solutions.
- Improve model performance through data preprocessing, cleaning, and analysis.
- Collaborate with research, product, and engineering teams to integrate ML solutions into production.
- Provide technical guidance, share best practices, and mentor team members.
Benefits
- Flexible working hours and workplace
- Open vacation policy