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About This Role
Grafana Labs is a remote\-first, open\-source powerhouse. There are more than 20M users of Grafana, the open source visualization tool, around the globe, monitoring everything from beehives to climate change in the Alps. The instantly recognizable dashboards have been spotted everywhere from a NASA launch and Minecraft HQ to Wimbledon and the Tour de France. Grafana Labs also helps more than 3,000 companies \- including Bloomberg, JPMorgan Chase, and eBay \- manage their observability strategies with the Grafana LGTM Stack, which can be run fully managed with Grafana Cloud or self\-managed with the Grafana Enterprise Stack, both featuring scalable metrics (Grafana Mimir), logs (Grafana Loki), and traces (Grafana Tempo).
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 opportunity and we would be interested in applicants from USA time zones only at this time.
Senior Software Engineer \- AI and Automation
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The Opportunity:
Grafana's Revenue Operations organization is looking for a Senior Software Engineer focused on building AI\-powered internal tools and agentic workflows for our GTM teams. You'll own the technical infrastructure that powers automation across Sales, Customer Success, and Marketing—from agentic skills that automate SDR outreach to backend services that connect AI models to Salesforce, Slack, BigQuery, and internal data platforms.
You'll be the technical architect behind systems that give every GTM team member leverage. Part systems builder and part problem detective, you'll identify bottlenecks across the business, design automated solutions using LLM APIs and orchestration frameworks, and ship tools that people actually use. Your work will directly shape how Grafana's GTM organization scales. You'll partner closely with Data Engineering, GTM Systems, Field Operations, and GTM leadership to build scalable, self\-service automation that reduces manual work and surface intelligence.
What You’ll Be Doing:
Agentic Tool Development
- Own end\-to\-end development of agentic and AI\-integrated workflows: design, implementation, testing, deployment, and maintenance
- Build modular, composable agentic systems using frameworks like LangChain, CrewAI, Anthropic MCP, or similar orchestration libraries
- Develop "agentic skills" for SDR and CSM teams—reusable capabilities that agents can invoke across interfaces (Slack, dashboards, internal apps)
- Implement observability and feedback loops: logging, performance metrics, prompt iteration, and model evaluation
Systems Integration \& Backend Services
- Build MCP servers, CLIs and APIs and microservices that connect AI models to business systems: Salesforce, BigQuery, Slack, HubSpot, email, calendars, analytics tools
- Architect data flows that enable retrieval\-augmented generation (RAG): connecting LLMs to internal knowledge bases, customer data in BigQuery, Salesforce data, and real\-time business context
- Build serverless or containerized services (GCP Cloud Functions, Cloud Run, or similar) that scale with usage and integrate with Grafana's cloud infrastructure
GTM Automation \& Workflow Manufacturing
- Scope high\-impact automation problems autonomously by shadowing Sales, Customer Success, and Marketing teams to identify efficiency gaps
- Design and deploy automation workflows using tools like n8n, Zapier, Prefect, or custom orchestration platforms
- Build systems designed for self\-service, with documentation and enablement materials that let others operate them independently.
Collaboration \& Technical Leadership
- Partner with GTM Analytics, Field Operations, Strategy \& Planning, and GTM Systems teams to scope, prioritize, and refine use cases
- Collaborate with Data Engineering to source and structure relevant data for agentic and AI\-enabled workflows
- Communicate technical constraints and trade\-offs clearly to non\-technical stakeholders across Revenue Operations and GTM leadership
- Establish and champion governance and compliance standards to AI workflows, including access controls, audit trails, and human\-in\-the\-loop escalation path, setting the bar for responsible use of sensitive customer and business data.
We invest heavily in developer productivity. You can use modern AI coding assistants as part of your daily workflow (your choice of tools, within security guidelines), backed by a company\-funded usage budget so you can iterate quickly without unnecessary friction. We encourage pragmatic AI\-assisted development: faster prototyping, test generation, refactors, documentation, and incident follow\-ups—always paired with strong code review and quality standards. You’ll also have access to frontier models (e.g., GPT\-Codex 5/3, Claude Opus 4\.6, Gemini 3 Pro).
What Makes You a Great Fit / Requirements:
- 5\+ years of software engineering experience, including backend development and systems integration work
- Strong proficiency in Python (preferred) or Javascript/Node.js
- Hands\-on experience with LLM APIs (OpenAI, Anthropic Claude, or similar) and orchestration libraries (LangChain, LlamaIndex, Anthropic MCP, Semantic Kernel, etc.)
- Comfortable building internal APIs, microservices, or serverless systems (GCP Cloud Functions, Cloud Run, AWS Lambda, or similar)
- Familiarity with SQL and data warehouses (BigQuery preferred)—you understand how to query, structure, and pipeline data for AI workflows
- Experience with authentication patterns, secure API handling, rate limiting, and workflow automation
- Proven ability to deliver AI\-powered features in production environments—you've shipped tools that real users depend on
- Strong problem selector who can identify high\-leverage initiatives and push back on low\-impact requests
- Thrives in ambiguous, fast\-moving projects; able to balance experimentation with engineering rigor
- Clear technical communicator—you can explain complex systems in simple terms and collaborate effectively with product and data stakeholders
- Comfort with autonomy—you identify the right questions, structure unstructured problems, and drive work independently
Bonus Points For:
- Experience with frontend frameworks \& tooling (React, Slack Block Kit, dashboard components) to build user\-facing interfaces for AI tools
- Familiarity with GTM platforms like Salesforce, HubSpot, Outreach, Gainsight, or similar CRM/sales engagement tools
- Experience with vector databases or retrieval pipelines (Pinecone, Weaviate, ChromaDB, pgvector, or similar)
- Prior work automating sales, customer success, or marketing workflows in a B2B SaaS environment
- Experience with workflow automation platforms like n8n, Prefect, Clay, PhantomBuster, Apify, Dust, or similar tools
- Familiarity with Model Context Protocol (MCP) or similar standards for connecting AI systems to data sources and tools
- Exposure to observability tools for AI systems (LangSmith, Weights \& Biases, custom logging/evaluation frameworks)
- Experience working in Revenue Operations, GTM Analytics, or Sales Operations environments
- Previous experience in open source or developer\-focused SaaS companies
- Familiarity with or exposure to graph databases and their use in RAG systems (Neo4J, Memgraph, Puppygraph)
Compensation \& Rewards:
In the United States, the Base compensation range for this role is USD 154,445 \- USD 185,334\. 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.
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 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: We will recruit, train, compensate and promote regardless of race, religion, color, national origin, gender, disability, age, veteran status, and all the other fascinating characteristics that make us different and unique. We believe that equality and diversity builds a strong organization and we’re working hard to make sure that’s the foundation of our organization 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 $154K-$185K range is below the median for AI Software Engineer roles in our dataset (median: $189K across 518 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 26,159 AI roles we're tracking, AI Software Engineer positions make up 2% of the market. At Grafana Labs, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $235,100 based on 665 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($169K) sits 28% below the category median. Disclosed range: $154K to $185K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Grafana Labs AI Hiring
Grafana Labs has 2 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Based in Remote, US. Compensation range: $185K - $209K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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