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Mid LevelFull-timePythonRustMachine LearningData ScienceGoML
Salary Range
$48,960 - $66,240 /year
This salary is estimated based on similar roles. The actual salary may vary.
About the Role
We are looking for an ML Platform Engineer to join the ML Platform team at Veriff. This role focuses on building the foundation for rapid, compliant, and reliable iteration on machine learning products. You will help bridge the gap between architectural vision and a seamless developer experience for data science teams.
Key Responsibilities
- →Implementing Observability Frameworks: Building tools and templates that provide visibility into model performance, data drift, and training statistics.
- →Engineering for Efficiency: Developing systems to track and optimize compute costs and training performance.
- →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.
Requirements
- ✓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.
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