Interested in this AI/ML Engineer role at Grafana Labs?
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About This Role
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, 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 Grafana Labs 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 grafana.com 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.
This is a remote position. We are looking for candidates in the the USA on Eastern time zones.
### About the Role
We’re looking for a Staff Product Analyst to join our growing Product Analytics team and serve as a strategic partner to Grafana’s AI organization. This role will support the teams building Grafana Assistant, our synchronous chat experience; Investigations, our asynchronous AI workflow for incident analysis; and AI observability solutions, including Sigil.
You will shape how the AI team measures success by defining KPIs, improving measurement practices, guiding experiment design and evaluation, and translating data into recommendations that influence product decisions.
This is a highly cross\-functional role. You’ll work closely with Product, Design, Engineering, and Data partners, with a primary focus on supporting the AI team, and report to the Product Analytics Manager
If you’re excited about building strong product analytics capabilities and using data to shape the future of AI\-powered experiences at Grafana, we’d love to hear from you.
You Will
- Partner cross functionally across Product to establish top\-level KPIs, dashboards, and product roadmap planning
- Define how success is measured for AI\-powered product experiences, including adoption, engagement, quality, retention, and user outcomes
- Guide the instrumentation of product data in collaboration with our Product Design, Product Management, and Engineering teams to enable reliable analysis of AI features and workflows.
- Design, evaluate, and interpret experiments to assess the impact of new AI capabilities and inform product decisions.
- Develop dbt models to transform and test our user behavior data in partnership with our Analytics Engineering team
- Influence best practices for Product Analytics across teams and help define standards as the company scales
- Contribute to defining the long\-term vision and strategy for Product Analytics, balancing near\-term needs with scalable, durable solutions.
You have
- Experience partnering across product and engineering organizations, and familiarity with typical product KPIs and frameworks (e.g., DAU/MAU, funnels, A/B testing)
- Experience defining and tracking success metrics for complex product areas, ideally including AI\-powered features or workflows.
- SQL: writing complex yet efficient SQL is a daily habit for you
- Data Visualization: experience with any of the following tools: Tableau, Looker, Omni, Hex, or of course, Grafana!
- Experience writing dbt models, implementing Product Analytics tooling (e.g., Heap, Pendo, Amplitude, FullStory), and code version control (git)
- Experience using AI\-assisted coding tools (e.g., Cursor, Claude Code) to quickly prototype and iterate across analysis, data modeling, instrumentation, visualization and code development.
- Excellent communication skills, able to explain technical topics to non\-technical audiences, and maintain many of the essential cross\-team and cross\-functional relationships necessary for the team’s success
A plus if you have
- Knowledge about observability
- Familiarity with Airflow or Prefect \- generating ad hoc ETL jobs
In the USA, the Base compensation range for this role is $162,275 \- $194,730 USD. Actual compensation may vary based on level, experience, and skillset as assessed in the interview process. Benefits include equity, bonus (if applicable) and other benefits listed here.
- *Compensation ranges are country specific. If you are applying for this role from a different location than listed above, your recruiter will discuss your specific market’s defined pay range \& benefits at the beginning of the process.*
- *Compensation ranges are country specific. If you are applying for this role from a different location than listed above, your recruiter will discuss your specific market’s defined pay range \& 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 a culture of collaboration and shared purpose.
- Scaling Organization – Tackle meaningful work in a high\-growth, ever\-evolving environment.
- Transparent Communication – Expect open decision\-making and regular company\-wide updates.
- Innovation\-Driven – Autonomy and support to ship great work and try new things.
- Open Source Roots – Built on community\-driven values that shape how we work.
- Empowered Teams – High trust, low ego culture that values outcomes over optics.
- Career Growth Pathways – Defined opportunities to grow and develop your career.
- Approachable Leadership – Transparent execs who are involved, visible, and human.
- Passionate People – Join a team of smart, supportive folks who care deeply about what they do.
- In\-Person onboarding \- We want you to thrive from day 1 with your fellow new ‘Grafanistas’ to learn all about what we do and how we do it.
- Balance is Key \- We operate a global annual leave policy of 30 days per annum. 3 days of your annual leave entitlement are reserved for Grafana Shutdown Days to allow the team to really disconnect. *\*We will comply with local legislation 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 which is 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.*
*Grafana Labs may utilize 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.*
\#LI\-Remote
*For information about how your personal data is used once you’ve applied to a job, check out our* *privacy policy**.*
Salary Context
This $162K-$194K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Grafana Labs, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($178K) sits 18% below the category median. Disclosed range: $162K to $194K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Grafana Labs AI Hiring
Grafana Labs has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $185K - $194K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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