Senior Machine Learning Engineer, Developer Advocacy | US | Remote

What You’ll Be Doing

Evolve the Interactive Learning Plugin's recommendation system

• Develop increasingly personalized approaches to candidate selection, ranking, sequencing, and next-best-action recommendations.

Own a real-time recommendation service

• Build and operate applied models: Develop, validate, version, monitor, and iterate on models used by the recommendation system.
• Own model training & serving.

Define what recommendation quality means

• Develop offline, online, and longitudinal measures of recommendation performance.
• 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.

What Makes You a Great Fit

Strong candidates should demonstrate credible ability across all three core areas below and be particularly strong in at least two.

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.

You should also be a strong product thinker 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

Compensation & Rewards

In the United States, the base compensation range for this role is $154,445 - $185,334. All of our roles include Restricted Stock Units (RSUs), giving every team member ownership in Grafana Labs' success.