Senior Machine Learning Engineer, Developer Advocacy
Tech Stack
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About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
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Communication @ 7
Data Science
Distributed Systems @ 4
Experimentation
Go @ 4
Grafana @ 3
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Machine Learning
Observability @ 3
TypeScript @ 4
gRPC @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Grafana Labs is hiring a Senior Machine Learning Engineer to lead the evolution of its Interactive Learning recommendation system. The role focuses on building a personalized, continuously improving system driven by real-time product behavior, content metadata, customer context, and experimentation.
This is a fully remote position for candidates in Canada and an applied product data science role. You will build, deploy, and operate recommendation models, design experiments, establish evaluation methodologies, and define the scientific roadmap. You will work closely with software engineers responsible for the production recommender codebase and with a Data Analyst supporting measurement, instrumentation, and analysis across Developer Advocacy.
Responsibilities
- Evolve the Interactive Learning Plugin's recommendation system.
- Develop personalized approaches to candidate selection, ranking, sequencing, and next-best-action recommendations.
- Own a real-time recommendation service.
- Develop, validate, version, monitor, and iterate on models used by the recommendation system.
- Own model training and serving.
- Develop offline, online, and longitudinal measures of recommendation performance.
- Own feature pipelines and monitoring of the model and architecture.
- Use existing data and infrastructure while identifying future instrumentation and platform requirements.
- Integrate incremental improvements into the existing recommender service.
- Partner with software engineers and data analysts to productionize models and integrate them safely into the recommender service.
- Collaborate with Product Analytics on metric definitions, instrumentation, data quality, dashboards, and experiment analysis.
- Work 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 to technical and non-technical audiences.
Requirements
- Experience building recommendation, ranking, search, matching, propensity, or next-best-action systems.
- Ability to start with simple, explainable approaches when appropriate.
- Experience with HTTP/gRPC, streaming, and distributed systems.
- Previous experience with Go and TypeScript.
- Experience personally building, validating, monitoring, and iterating on models used in a product or operational environment.
- Ability to work in version-controlled codebases and collaborate with engineers on production implementation.
- Strong product thinking and technical communication skills.
- Ability to work with ambitious and ambiguous objectives, identify key unknowns, and create a sequence of models and experiments that improves the product.
Bonus Qualifications
- 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.
Compensation and Benefits
- Base compensation in Canada: $164,490 CAD–$197,389 CAD per year.
- Restricted Stock Units (RSUs) are included with all roles.
- 100% remote work and a global culture.
- Career growth pathways.
- In-person onboarding.
- Global annual leave policy of 30 days per annum, including three Grafana Shutdown Days.
- Equal opportunity employer.