The ML Platform team at Veriff builds the foundation for rapid, compliant, and reliable iteration on machine learning products. We provide the scalable, observable, and user-friendly systems required to manage data, train models, evaluate performance, and deploy models at scale.

Having already established our core platform capabilities, we are now entering a high-growth phase focused on systemic excellence: institutionalizing world-class observability, optimizing for cost-efficiency, and radicalizing our experimentation speed. Your role will help us bridge the gap between architectural vision and a seamless developer experience for our data science teams.

You’ll help us enable ML innovation by:

• Implementing Observability Frameworks: Building the tools and templates that provide visibility into model performance, data drift, and training statistics, ensuring our continuous retraining loops are robust.
• Engineering for Efficiency: Developing systems to track and optimize compute costs and training performance, allowing us to scale our ML efforts sustainably.
• Building Experimentation Tooling: Executing on the roadmap for internal tools that enable Data Scientists and ML Engineers to iterate and deploy experiments with minimal friction.
• Developing SaaS-grade ML Services: Writing high-quality, maintainable Python code to build and automate services that sit at the core of our ML lifecycle.
• Bridge-Building: Working alongside our Staff Engineer to implement architectural designs and collaborating with SRE/DevXP teams to ensure our solutions are production-ready and easily managed.

You are the right future Veriffian for the job if you have:

• 3+ years of experience in software or ML engineering, specifically building tools that support the ML lifecycle (MLOps).
• Strong Python skills with experience in building internal APIs or automation services.
• Hands-on experience with the open-source ML stack (e.g., MLflow, Kubeflow, Ray, or Prometheus/Grafana for ML monitoring).
• A "Product" mindset for internal tools: You care about the developer experience of the Data Scientists using your platform.
• Experience with SQL and Data Engineering (e.g., Snowflake, Spark, or dbt) to understand how data flows into our training pipelines.
• A skeptical, first-principles approach to engineering—you prefer understanding the "why" behind a system rather than just following a vendor's tutorial.

• Flexibility to work from home
• Stock options that ensure your share in our success
• Extra recharge days on top of your annual vacation
• Comprehensive relocation support to Estonia or Spain
• Extensive medical, dental, and vision insurance to ensure you’re feeling great physically and mentally
• Learning and Development & Health and Sports budget that you are free to tailor to your own needs
• Four weeks of fully paid sabbatical leave after reaching your 5th work anniversary