About the job
The Machine Learning (ML) Platform team at Veriff is at the forefront of creating a robust foundation for the rapid and compliant development of machine learning products. We deliver scalable, observable, and user-friendly systems that are essential for managing data, training models, evaluating performance, and deploying models on a large scale.
Having laid the groundwork with our core platform capabilities, we are now poised for significant growth, emphasizing systemic excellence. Our focus includes institutionalizing world-class observability, maximizing cost efficiency, and accelerating our experimentation processes. In this role, you will play a crucial part in aligning our architectural vision with a seamless developer experience for our data science teams.
Your Contributions will Drive ML Innovation by:
- Implementing Observability Frameworks: Crafting tools and templates that enhance visibility into model performance, data drift, and training metrics, ensuring our continuous retraining processes are robust.
- Engineering for Efficiency: Designing systems to monitor and optimize computing costs and training performance, enabling sustainable scaling of our ML initiatives.
- Building Experimentation Tooling: Executing the roadmap for internal tools that empower Data Scientists and ML Engineers to iterate and deploy experiments with ease.
- Developing SaaS-grade ML Services: Writing high-quality, maintainable Python code to construct and automate services integral to our ML lifecycle.
- Bridge-Building: Collaborating with our Staff Engineer to execute architectural designs and partnering with SRE/DevXP teams to ensure our solutions are production-ready and manageable.
