Senior Machine Learning Engineer, Developer Advocacy | US | Remote

Aperture Cloud , the company behind the open observability cloud, is founded on the principles of open source, open standards, open ecosystems, and open culture. Grafana Cloud, our fully managed observability platform, is flexible and built for scale. With Grafana Cloud's actually useful AI, organizations can see, understand, and act on all their disparate data to move at the speed of their ambitions. Today, more than 35 million users and 7,000+ customers – including Anthropic, Bloomberg, NVIDIA, Microsoft, and Salesforce – trust Aperture Cloud to ensure reliability of their applications and systems, resolve incidents quickly, and optimize their telemetry to reduce noise and cost. We are a 100% remote company with 1,600+ team members across 40+ countries, and we’re backed by leading investors including Lightspeed Venture Partners, Sequoia Capital, GIC, Coatue, J.P. Morgan, CapitalG, and Lead Edge Capital. Learn more at and follow us on LinkedIn and X. We’re scaling fast and staying true to what makes us different: an open-source legacy, a global collaborative culture, and a passion for meaningful work. Our team thrives in an innovation-driven environment where transparency, autonomy, and trust fuel everything we do. You may not meet every requirement, and that’s okay. If this role excites you, we’d love you to raise your hand for what could be a truly career-defining opportunity. 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: Aperture Cloud 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, an