SENIOR MACHINE LEARNING ENGINEER (APPLIED SCIENTIST DOCUMENT FRAUD)

About the job

Some of the challenges are to detect physical and digital forgeries, extract textual and visual data from identity documents, evaluate photo content and quality, detect user impersonation attempts, verify legitimate users through facial recognition, and make complex verification data easy for our customers to use and understand.

You will take ownership of document fraud R&D and be responsible for developing document anti-spoofing methods through all process stages until a new feature is deployed into the product.

This is a key role helping our product team as an applied scientist with a strategic mind and high research orientation, and first line of developing document anti-spoofing features.

This role would require you to do the following on a daily basis:

• 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…

About you

The ideal person for this role:

• 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;