Senior Machine Learning Engineer, Developer Advocacy | Canada | Remote

Grafana Labs

20d•CA$164,490 - CA$197,389•Remote

About The Position

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. This role will lead the evolution of the current rule-based recommendation engine 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 where you will 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.

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 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.

Nice To Haves

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

Responsibilities

Evolve Pathfinder’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.