Machine Learning Engineer III

Role Value

As a Machine Learning Engineer III, you will play a key role in shaping advanced biometric and computer visiondriven AI systems. Your expertise in machine learning, deep learning, and computer vision will drive the development of innovative algorithms used across industries such as Financial Services, Travel, Sharing Economy, Fintech, and Gaming. By deploying and maintaining these models in production, you will ensure the robustness, reliability, and real-time performance of mission-critical AI solutions.

T-Shaped Engineering Expectation

Beyond your deep specialization in machine learning, you are expected to adopt a T-shaped engineering mindset, contributing to broader engineering processes such as data engineering, software development, and testing. You will take end-to-end ownership of the models you develop—from experimentation and validation to deployment and production monitoring. Your work will be instrumental in delivering secure, scalable, and high-performing AI capabilities that support growing global demands.

Example Responsibilities

• Design and implement machine learning, deep learning, and classical computer vision algorithms focused on liveness detection.
  
• Conduct research to support the deployment of advanced algorithms.
  
• Develop, evolve, and maintain production-quality code.
  
• Deploy models as AWS SageMaker endpoints or directly onto devices.
  
• Stay updated with advancements in deep learning, computer vision, and biometric technologies through academic research and industry events.
  
• Collaborate closely with engineers and product managers in an Agile development environment.
  

Experience and Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field.
  
• Minimum of 5+ years of commercial experience in computer vision or a related area (2 years with a Master’s).
  
• Strong proficiency in computer vision and deep learning techniques.
  
• Hands-on experience with PyTorch/TensorFlow, OpenCV, SKLearn, and production-grade Python.
  
• Experience implementing best practices for monitoring and maintaining ML models in production.
  
• Proven experience building, training, and deploying models to production environments.
  
• Excellent communication and problem-solving skills.
  

Great to Have

• Experience with liveness detection or presentation attack detection.
  
• PhD with specialization in computer vision or deep learning.
  
• Experience developing facial processing or facial recognition algorithms.
  
• Exposure to voice, hand recognition, or other biometric modalities.
  
• Experience deploying models on edge devices.
  
• Experience with C++.