Senior Machine Learning Engineer, Developer Advocacy | US | Remote

Senior ML Engineer Recommender Systems, Developer Advocacy | US | Remote

This is a fully remote position and we're considering candidates in the US.

The Opportunity:

Grafana Labs is building an Interactive Learning system, an open source, in-product learning experience that helps users learn and succeed without leaving Grafana. A central part of that vision is a personalized recommendation system that helps each user discover the next guide, action, or product experience most likely to help them succeed.

Today, the Interactive Learning tool includes a rule-based recommendation engine that provides useful contextual recommendations. We are hiring an ML Engineer to lead its evolution into an increasingly personalized, continuously improving system driven by real-time product behavior, content metadata, customer context, and experimentation.

This is an applied product data science role. You will personally build, deploy, and operate recommendation models, design experiments, establish evaluation methodology, and define the scientific roadmap. You will partner closely with software engineers who own the production recommender codebase and with an existing Data Analyst who supports measurement, instrumentation, and analysis across Developer Advocacy.

What You’ll Be Doing:

The long-term vision is ambitious, but we do not expect it to arrive in one release. We are looking for someone who can understand the whole problem, establish strong foundations, and ship measurable improvements into the existing recommender one iteration at a time.

• 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.

What Makes You a Great Fit:

We know it is rare to find everything. 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. Actual compensation may vary based on level, experience, and skillset as assessed throughout the interview process. All of our roles include Restricted Stock Units (RSUs), giving every team member ownership in Grafana Labs' success. We believe in shared outcomes—RSUs help us stay aligned and invested as we scale globally.