Senior Machine Learning Engineer (Developer Advocacy)

• 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

Benefits

• Vacation: Balance is key. Our team enjoys 30 days of paid vacation each year on top of national holidays, parental leave, and sick leave. We also take a breather on a number of Grafana Shutdown Days each year
• Healthcare: We’re proud to provide health coverage or stipends for our colleagues in the US, UK, Canada, the Netherlands, Sweden, Singapore, and India
• Retirement planning: There’s no time like the present to start saving for your future. We make employer contributions into the pension pots of our team members in the US, UK, Canada, the Netherlands, Sweden, and Germany
• Professional development: On top of a $1,500 annual learning and development stipend, Grafanistas have thousands of on-demand courses at their fingertips to help them grow professionally. Want to attend a conference or training? Go ahead. Just pass on what you learned
• Work location: Vast majority of our roles are fully remote, focused on hiring the best talent and allowing you to perform from the comfort of your home. If you fancy a change of scene, we’ll also reimburse you up to $175 a month for a personal co-working space
• Choice of tech: There’s no one-size-fits-all when it comes to the tech required to do your job. Choose the laptop and accessories you need when you join us, and we’ll refresh them every three years
• Mindfulness: When you join the team, you can sign up for a complimentary subscription to Headspace to take advantage of the benefits of mindfulness and meditation. Our wellbeing resource group also organize sessions run by fellow Grafanistas or external trainers
• Global Employee Assistance Program: We offer all team members a 100% confidential support service with 24/7 365 access to professionally qualified counsellors and specialists
• Paid parental leave: Grafana offers paid parental leave to all eligible new parents. This offers Grafanistas time to bond with and care for their children in the first year after birth or adoption- 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
• HTTP/gRPC, streaming, Go/TypeScript previous experience in distributed systems
• 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
• 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
• 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
• Experience with SaaS product telemetry and customer-account data
• Experience with content, education, onboarding, or learning recommendation systems
• Experience using warehouse-scale behavioral data
• Experience with directed graphs, sequence models, or prerequisite-aware recommendations
• Experience with contextual bandits or other exploration strategies
• Experience with open source software or transparent development practices
• Familiarity with Grafana or the broader observability ecosystem
• Experience working with privacy, fairness, explainability, or responsible personalization constraints