About the job
About GBG
Empowering Safe and Rewarding Digital Experiences for Real People Worldwide
At GBG, we are dedicated to broadening access to digital opportunities for authentic individuals, while enabling businesses to connect with genuine customers. Our advanced technology leverages diverse and credible data sources to establish a definitive reference point for identity and address verification.
With over three decades of industry experience, our team is committed to fostering safe and rewarding digital interactions for everyone. We believe that people of all ages, locations, and backgrounds should have the ability to digitally validate their identity and residence.
About the Team and Role
The CVML Teams
As a core component of GBG's Documents and Biometrics portfolio, our team is dedicated to developing innovative and robust artificial intelligence models aimed at transforming KYC verification for our clients. We are at the forefront of advancing these state-of-the-art technologies, striving to deliver exceptional solutions for document verification and digital trust. Our collaborative approach harnesses a wide range of expertise to achieve collective success, and we operate within an Agile framework to enhance efficiency through automation.
Senior Machine Learning Engineer
The Senior Machine Learning Engineer is a pivotal contributor responsible for architecting, building, deploying, and continuously refining machine learning and computer vision models that drive production-grade systems. This position demands a blend of hands-on technical proficiency, mentorship, collaborative efforts, and data-driven problem-solving capabilities.
Working in an Agile setting, the Senior ML Engineer collaborates with the machine learning team and cross-functional partners to translate product requirements into robust ML solutions. The role necessitates deep expertise in contemporary ML and computer vision methodologies, experience in operating models in production environments, and the ability to mentor junior engineers through the entire ML lifecycle while driving significant improvements in model performance and product quality.
Key Responsibilities
Technical Development & Innovation
- Design, implement, and optimize cutting-edge machine learning and computer vision models to elevate product capabilities.
- Conduct research, evaluation, and application of modern architectures and techniques, including CNNs, transformers, and vision-language models.
- Implement and benchmark newly developed algorithms on large-scale datasets, validating both accuracy and throughput.
- Refine large-scale models utilizing efficient adaptation techniques such as LoRA and QLoRA.
Model Evaluation & Data Analysis
- Define, implement, and monitor suitable evaluation metrics (e.g., precision, recall, ROC-AUC, confusion matrices).
- Analyze training data and model performance to drive improvements.