Machine Learning Engineer
Role details
Job location
Tech stack
Job description
- Innovate & Optimize - Develop and refine state-of-the-art deep learning models for computer vision applications such as facial recognition, liveness detection, and document processing and validation.
- Production-Ready Solutions - Architect and maintain scalable, efficient ML pipelines and ensure production-readiness and cost efficiency of deployed models.
- Research & Discovery - Stay at the forefront of academic and industry breakthroughs, experiment with emerging architectures and algorithms, and translate research into production-grade solutions.
- Data Mastery - Drive improvements in model performance through robust data preprocessing, cleaning, and analysis workflows.
- Collaborate & Communicate - Partner closely with research, product, and engineering teams to integrate ML solutions seamlessly into Incode's production environment.
- Mentorship & Leadership - Provide technical guidance, share best practices, and contribute to building a culture of innovation and continuous learning., * Elite Team & Technology - Collaborate with top-tier engineers, researchers, and scientists at the forefront of AI.
- Ownership & Autonomy - Operate with the freedom to innovate, test, and deploy your ideas end-to-end.
- Global Impact - Your models will power secure, frictionless identity verification experiences across continents.
Aspects of our Culture:
- High performance
- Freedom & responsibility
- Context, not control
- Highly aligned, loosely coupled
- Continuous Feedback
- Promotions & Development
- Learn more about Life at Incode!
Benefits & Perks:
- Flexible Working Hours & Workplace
- Open Vacation Policy
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.
- Demonstrated experience developing, deploying, and optimizing models for both performance and cost efficiency in production environments (including edge AI).
- Specialized expertise in at least one of the following areas:
- Facial Recognition - face recognition, age estimation, facial attributes analysis.
- Liveness Detection - facial authentication, anti-spoofing, and anti-deepfake defense.
- Document Processing - OCR-based extraction, document analysis, and quality assurance.
- ID Verification - biometric data authentication and document integrity validation.
-
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 thriving in both long-term research and rapid iteration cycles.
-
Deep curiosity and technical excellence in AI and computer vision.
-
Builder mindset with a drive to deliver real-world impact through applied research.
-
Strong analytical thinking balanced by practical execution.
-
Collaborative spirit and ability to simplify complex ideas for diverse audiences.
-
Commitment to innovation, scalability, and continuous improvement.