Senior Machine Learning Engineer, Developer Advocacy

Details

Responsibilities

• Evolve the Interactive Learning Plugin's recommendation system
  • Develop increasingly personalized approaches to candidate selection, ranking, sequencing, and next-best-action recommendations.
  • You’ll own a real-time recommendation service
• Build and operate applied models
  • Develop, validate, version, monitor, and iterate on models used by the recommendation system.
  • You’ll own model training & serving
• Define what recommendation quality means
  • Develop offline, online, and longitudinal measures of recommendation performance.
  • You’ll own feature pipelines, monitoring of the model and architecture
• Ship incremental improvements
  • Use the data and infrastructure available today while identifying the instrumentation and platform capabilities needed tomorrow.
  • Integrate improvements into the existing recommender rather than waiting for a complete replacement system.
• Partner across disciplines
  • Work closely with software engineers & data analysts to productionize models and integrate them safely into the recommender service.
  • Partner with the Product Analytics team on metric definitions, instrumentation, data quality, dashboards, and experiment analysis.
  • Collaborate with Developer Advocacy, Docs, Product, Engineering, GTM, and other teams to translate ambiguous needs into testable hypotheses and measurable product decisions.
  • Explain modeling choices, tradeoffs, uncertainty, and results clearly to both technical and non-technical audiences.

Requirements

• Recommendation and personalization science: you have built recommendation, ranking, search, matching, propensity, or next-best-action systems. You are comfortable beginning with simple, explainable approaches when they are the best way to learn.
• HTTP/gRPC, streaming, Go/TypeScript previous experience in distributed systems
• Applied model ownership: you have personally built, validated, monitored, and iterated on models used in a product or operational environment. You can work effectively in version-controlled codebases and collaborate with engineers on production implementation.
• Strong product thinking and technical communicator: you can take an ambitious and ambiguous objective, identify the most important unknowns, and create a sequence of models and experiments that steadily improves the product.

Bonus Points For

• Experience with content, education, onboarding, or learning recommendation systems
• Experience with SaaS product telemetry and customer-account data
• Experience using warehouse-scale behavioral data
• Experience with directed graphs, sequence models, or prerequisite-aware recommendations
• Experience with contextual bandits or other exploration strategies
• Familiarity with Grafana or the broader observability ecosystem
• Experience with open source software or transparent development practices
• Experience working with privacy, fairness, explainability, or responsible personalization constraints

Benefits

• 100% Remote, Global Culture
• Scaling Organization
• Transparent Communication
• Innovation-Driven
• Open Source Roots
• Empowered Teams
• Career Growth Pathways
• Approachable Leadership
• Passionate People
• In-Person onboarding
• Balance is Key: global annual leave policy of 30 days per annum (3 days reserved for Grafana Shutdown Days)

Compensation & Rewards

• In Spain, the base compensation range for this role is EUR 82,988 - EUR 99,586.
• All roles include Restricted Stock Units (RSUs).