Senior Machine Learning Engineer, Developer Advocacy

About Grafana Labs

Grafana Labs, 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, organisations can see, understand, and act on 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 Grafana Labs to ensure the reliability of their applications and systems, resolve incidents quickly, and optimise their telemetry to reduce noise and cost.

We are a 100% remote company with 1,600+ team members across 40+ countries, backed by leading investors including Lightspeed Venture Partners, Sequoia Capital, GIC, Coatue, J.P. Morgan, CapitalG, and Lead Edge Capital.

We’re scaling fast while staying true to what makes us different: an open-source legacy, global collaborative culture, and 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

Location: Germany | Remote

This is a fully remote position and we’re considering candidates in Germany.

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 personalised 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’re hiring an ML Engineer to lead its evolution into an increasingly personalised, continuously improving system driven by real-time product behaviour, 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 methodologies, and define the scientific roadmap.

You’ll 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 don’t expect it to arrive in one release. We’re 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 personalised approaches to candidate selection, ranking, sequencing, and next-best-action recommendations.
• Own a real-time recommendation service: Build and operate the systems that deliver personalised recommendations in production.
• Build and operate applied models: Develop, validate, version, monitor, and iterate on models used by the recommendation system.
• Own model training and serving: Build the pipelines and infrastructure required to train and serve recommendation models reliably.
• Define what recommendation quality means: Develop offline, online, and longitudinal measures of recommendation performance.
• Own feature pipelines and monitoring: Ensure the data, models, and architecture supporting the recommender are observable and reliable.
• 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: Improve the current system rather than waiting for a complete replacement.
• Partner across disciplines: Work closely with software engineers and data analysts to productionise models and integrate them safely into the recommender service.
• Partner with Product Analytics: Collaborate on metric definitions, instrumentation, data quality, dashboards, and experiment analysis.
• Collaborate cross-functionally: Work with Developer Advocacy, Docs, Product, Engineering, GTM, and other teams to translate ambiguous needs into testable hypotheses and measurable product decisions.
• Communicate clearly: Explain modelling choices, trade-offs, uncertainty, and results to both technical and non-technical audiences.

What Makes You a Great Fit

We know it’s 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 personalisation science: You have built recommendation, ranking, search, matching, propensity, or next-best-action systems. You’re comfortable beginning with simple, explainable approaches when they are the best way to learn.
• Distributed systems experience: Experience with HTTP/gRPC, streaming, Go, and/or TypeScript 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.
• Product thinking and technical communication: 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 behavioural 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 personalisation constraints.

Compensation & Rewards

In Germany, the base compensation range for this role is EUR 97,034–EUR 116,441. 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.

Compensation ranges are country-specific. If you are applying from a different location than listed above, your recruiter will discuss your specific market’s defined pay range and benefits at the beginning of the process.

Why You’ll Thrive at Grafana Labs

• 100% Remote, Global Culture: As a remote-only company, we bring together talent from around the world, united by collaboration and shared purpose.
• Scaling Organisation: Tackle meaningful work in a high-growth, ever-evolving environment.
• Transparent Communication: Expect open decision-making and regular company-wide updates.
• Innovation-Driven: Enjoy autonomy and support to ship great work and try new things.
• Open Source Roots: Work in an organisation shaped by community-driven values.
• Empowered Teams: Experience a high-trust, low-ego culture that values outcomes over optics.
• Career Growth Pathways: Access defined opportunities to grow and develop your career.
• Approachable Leadership: Work with transparent executives who are involved, visible, and human.
• Passionate People: Join smart, supportive colleagues who care deeply about what they do.
• In-Person Onboarding: Start strong alongside fellow new Grafanistas, learning about what we do and how we do it.
• Balance Is Key: We operate a global annual leave policy of 30 days per annum, with 3 days reserved for Grafana Shutdown Days so the team can truly disconnect. Local legislation applies where applicable.

Equal Opportunity Employer

Grafana Labs is an equal opportunities employer. We welcome applications from everyone regardless of race, colour, nationality, origin, caste, sex, gender reassignment identity or expression, sexual orientation, age, religion or belief, disability, veteran status, genetic information, pregnancy, maternity, marital, family or carer status, or any other characteristic protected by local law.

We believe that equality and diversity build a strong organisation, and we work hard to ensure that is the foundation of our organisation as we grow.

AI in Recruitment

Grafana Labs may utilise AI tools in its recruitment process to assist in matching information provided in CVs to job postings. The recruitment team will continue to review inbound CVs manually to identify alignment with current openings.