SENIOR MACHINE LEARNING ENGINEER (APPLIED SCIENTIST DOCUMENT FRAUD)

Alice Biometrics is a biometric identity verification solution company focused on reducing identity fraud and maximizing conversion rates through advanced technology. They are seeking a Senior Machine Learning Engineer to take ownership of document fraud R&D and develop document anti-spoofing methods, contributing to the product team's objectives with a strategic and research-oriented approach.

Responsibilities:

• Formulate innovative approaches to combat document fraud and reduce risk with the powers of ML
• Create new features, train new models, deploy them into production environment
• Contribute by extending and improving our ML frameworks and platform, creating next-generation capabilities
• Build and deploy solutions to interesting computer vision or machine learning problems including document data extraction, fraud detection or biometric verification challenges
• Support and guide other engineers in learning about, applying and delivering product features driven by machine learning techniques
• Work alongside other machine learning and computer vision specialists in order to deliver on both short term objectives and long term goals
• Help develop robust model training and data infrastructure to support continual optimisation of ML-driven approaches
• Assist in steering the ML-led development across the tech team
• Gathering of information about new tech trends from academic articles, journals, code repositories…

Requirements:

• PhD degree in Computer Science (or related quantitative field) or MS degree in Computer Science with related experience
• 5+ years of experience building machine learning systems in production, and with real-time technology problems
• Strong academic and publication record
• Experience with cloud-based training and deployment pipelines
• Experience training Neural Net architectures for classification, object detection, and segmentation
• Excellent coding skills (Python is essential, C++ is considered a plus)
• Proficiency with some of these machine vision and machine learning frameworks: OpenCV, TensorFlow + Keras, TensorRT, PyTorch, Pytorch + FastAI. With a strong portfolio of development examples
• Good working knowledge of the tools in our dev stack, including Git, Google AI Cloud, Docker, and Kubernetes
• Solid understanding of statistics, probability, linear algebra & calculus
• Hands on experience working on computer vision and machine learning projects e.g. face verification, object detection and/or classification
• Comfortable reading, discussing, and applying research from published papers
• Communication is important so we expect you to be able to translate complex ideas into understandable content
• A pro-active, self-managing attitude
• Transformers management framework, such as BERT, is a plus
• A plus Linux, Redis, and ELK stack