Senior Machine Learning Engineer (Developer Advocacy)

Role overview

Join Grafana Labs as a Senior Machine Learning Engineer (Developer Advocacy) and help evolve the Interactive Learning recommendation system. You’ll build, deploy, and operate recommendation models, design experiments, and define evaluation methods—working closely with software engineers and data analysts to integrate models into production.

Key missions

• Evolve the recommendation system for the interactive learning plugin with increasingly personalized selection, ranking, sequencing, and action recommendations
• Build, deploy, and operate recommendation models
• Design experiments and establish a scientific evaluation methodology and research roadmap
• Collaborate with engineers and data analysts to productionize and integrate models safely into the recommender service

Responsibilities

• Understand end-to-end the recommendation problem and ship measurable improvements iteratively
• Own model lifecycle: build, validate, monitor, and iterate on production models
• Turn ambiguous objectives into a prioritized sequence of models and experiments
• Work effectively in version-controlled codebases and collaborate on production implementation with engineers

Requirements

• Strong capability across:
  • Recommendation & personalization science (recommendation, ranking, search/matching, propensity, next-best-action). Comfortable starting with simple explainable approaches when appropriate.
  • Applied model ownership (built/validated/monitored/iterated models in product or operational environments)
  • Distributed systems experience: HTTP/gRPC, streaming, and Go/TypeScript experience
• Strong product thinking and technical communication

Nice-to-haves

• Experience with content/education/onboarding/learning recommendation systems
• SaaS telemetry and customer-account data; warehouse-scale behavioral data
• Contextual bandits and exploration strategies
• Directed graphs, sequence models, prerequisite-aware recommendations
• Privacy, fairness, explainability, and responsible personalization
• Open source experience or transparent development practices
• Familiarity with Grafana or the broader observability ecosystem