Machine Learning Engineer
Incode is an AI-driven identity verification and fraud-prevention company. It provides enterprise tools for identity and age verification, KYC/KYB/AML workflows, deepfake detection, workforce identity protection, and fraud analytics.
Maintainer signals as of 8/14/2026
Funding history
About Incode
Incode Technologies provides an enterprise identity orchestration platform that helps organizations verify customers, businesses, employees, and autonomous AI systems. Its products combine biometric and document verification, liveness and deepfake detection, fraud intelligence, analytics, case management, APIs, SDKs, and no-code workflow tools. It serves large organizations across financial services, healthcare, online gaming and gambling, e-commerce, public sector, and social media.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will design, build, and deploy deep learning models for computer vision applications including facial recognition, liveness detection, and document processing. You will architect and maintain scalable ML pipelines, optimize models for performance and cost (including edge deployments), conduct research and experiment with new architectures, improve data preprocessing and analysis workflows, collaborate with cross-functional teams to integrate solutions into production, and mentor engineers while sharing technical best practices.
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 models for performance and cost efficiency in production environments including edge AI
- Specialized expertise in at least one of facial recognition liveness detection document processing or ID verification
- Proven ability to design and maintain end-to-end ML pipelines
- Track record of research contributions or publications in deep learning and computer vision
- Excellent communication and collaboration skills across cross-functional teams
- Passion for mentoring talent and working in both long-term research and rapid iteration cycles
Responsibilities
- Develop and refine state-of-the-art deep learning models for facial recognition liveness detection and document processing
- Architect and maintain scalable efficient ML pipelines and ensure production readiness and cost efficiency of deployed models
- Research and experiment with emerging architectures and translate research into production-grade solutions
- Improve model performance through robust data preprocessing cleaning and analysis
- Collaborate with research product and engineering teams to integrate ML solutions into production
- Mentor and provide technical guidance to team members
Benefits
- Flexible working hours and workplace
- Open vacation policy