Grafana Labs - Senior Machine Learning Engineer, Developer Advocacy | Germany | Remote

• 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. • Recommendation and personalization science • 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. • Applied model ownership • 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.