Staff Backend Engineer - Second Horizon | US | Remote
for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We must only include sections where there is real specific content. We must not repeat job title as heading. No Job ID etc. Remove boilerplate like EEO, etc. Also remove application instructions. Must preserve all substantive info.
We need to parse the posting.
Let's extract sections:
- About the role: description of company, initiative, role description. Could be a paragraph.
- Responsibilities: list of key responsibilities.
- Requirements: what we're looking for (maybe separate from Requirements and Nice to Have). The posting has "What We're Looking For" with bullet points (Strong engineering skills, AI experience, Quick iteration, Proven initiative, Collaborative attitude). Then "Requirements" with bullet list (Experience with LLMs, prompt engineering, building applications powered by GenAI; Proven track record delivering software into production; Exposure to cloud-native environments; Experience using observability tools). Then "Nice To Have" list.
- Benefits: The posting mentions "Benefits include equity, bonus (if applicable) and other benefits listed here." Also later "Why You’ll Thrive At Grafana Labs" includes many culture points, remote, global, scaling org, transparent communication, innovation-driven, open source roots, empowered teams, career growth pathways, approachable leadership, passionate people, in-person onboarding, balance is key (30 days annual leave, 3 days shutdown). Also earlier: "We are a 100% remote company with team members across 40+ countries". Also "This is a remote opportunity and we are looking for candidates from the U.S. or Canada. Residents of Quebec are not eligible for this role." That's location eligibility.
- Pay: "In the US, the Base compensation range for this role is $174,986 - $209,983. Actual compensation may vary based on level, experience, and skillset as assessed in the interview 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."
- Schedule: Not explicitly mentioned. There's no schedule info. So we should not create a Schedule section.
- Skills: Could be derived from requirements but we already have Requirements and Nice to Have. The instruction says use headings like Skills if there is content. We could combine Requirements and Nice to Have under Requirements? But they said use headings for sections like About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Qualifications maybe similar to Requirements. We'll decide to have sections: About the role, Responsibilities, Requirements (including both required and nice-to-have), Benefits, Pay. Possibly also Qualifications but we can skip if redundant.
We must not create sections with filler like "commensurate with experience". We have actual content.
We need to use - for any list of duties/requirements/benefits. Use
for prose.
We must not include markdown fences. Just raw HTML.
Let's craft.
First, About the role: combine company description, initiative, role description. Could be multiple paragraphs but we can put in one
with multiple sentences.
Responsibilities: list from "Key Responsibilities". We'll create
- each.
Requirements: combine "What We're Looking For" and "Requirements" and "Nice To Have"? Might be too much. Perhaps separate: Requirements (mandatory) and Nice To Have (optional). But headings allowed: Requirements, Skills. Could treat "What We're Looking For" as Skills/Qualifications. But we have both Requirements and Nice To Have. Let's create two sections: Requirements (mandatory) and Nice To Have (optional). However instruction says use headings like Skills etc. We'll include Requirements and Nice To Have as separate headings (
) if they have content. That's okay.
Benefits: list from "Why You’ll Thrive At Grafana Labs" plus earlier benefits (equity, bonus). We'll create - each benefit.
Pay:
with compensation range.
We must not include boilerplate like EEO, privacy policy, etc.
Let's extract specifics.
About the role prose:
"Grafana Labs is the company behind Grafana Cloud, the fully managed observability platform trusted by more than 10,000 organizations to ensure reliability, resolve incidents faster, and optimize telemetry at scale. Built on open source and open standards and designed for interoperability across any stack, Grafana Cloud brings AI to observability and observability to AI, giving teams (and their agents) unified visibility so they can see, understand, and act on all their disparate data, wherever it lives, and move at the speed of their ambitions. Customers, including Anthropic, Bloomberg, NVIDIA, Microsoft, and Salesforce, rely on Grafana Labs. We are a 100% remote company with 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. 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 opportunity and we are looking for candidates from the U.S. or Canada. Residents of Quebec are not eligible for this role. At Grafana Labs, we build observability tools that help users understand, respond to, and improve their systems – regardless of scale, complexity, or tech stack. We recently started a skunkworks initiative with a mission to bring observability to the rest of the business. Our goal is to make Grafana the single best place where humans and AI agents understand and act on real-time data from across the enterprise. As part of this initiative, we are building an AI-native data intelligence system that gives agents reliable, governed access to enterprise context. This system helps agents retrieve the right data, metadata, definitions, lineage, quality signals, and institutional knowledge without every agent builder having to maintain their own brittle context files. The principle is simple: the context system retrieves, agents decide, and data teams maintain the intelligence. The new team is a mix of seasoned Grafanistas and new hires. We operate with a high degree of autonomy and ownership, both as individuals and as a team. Engineers are empowered to make decisions, move quickly, and validate ideas early – while being supported by a deeply collaborative culture that values curiosity, feedback, and cross-functional partnership. We’re looking for a Staff-level Backend Engineer to help build the first production services for this context layer management system. You will design and ship the core backend architecture that powers ingestion, context storage, retrieval APIs, and agent-facing integrations. This is an early-stage role on a high-autonomy team, so you should be comfortable working through ambiguity, making pragmatic architectural decisions, and building systems that can evolve from internal dogfooding to production-grade SaaS. As the team matures, there’s a broad opportunity to expand or redefine this role based on impact and initiative."
That's a lot but we can keep as prose.
Responsibilities list:
- Build the core backend services: Design, implement, test, and operate the first services for context ingestion, context indexing, retrieval orchestration, API access, source configuration, and system administration.
- Create a scalable SaaS foundation: Help define and build the architecture for a multi-tenant service, including tenant isolation, usage tracking, quotas, audit logs, background jobs, and reliable service boundaries.
- Power agent-facing retrieval workflows: Build APIs and service interfaces that allow AI agents, MCP tools, CLIs, and internal applications to retrieve relevant context, provenance, confidence signals, and warnings.
- Work across product and infrastructure: Partner with the team to make practical tradeoffs between fast experimentation and long-term reliability, especially as the project moves from prototype to production.
- Operate what you build: Instrument services with metrics, logs, traces, alerts, and dashboards. Use observability tools to understand system behavior and improve reliability.
- Contribute to technical direction: Help shape the architecture, service boundaries, storage choices, API contracts, deployment patterns, and engineering practices for a new product area.
- Effective communication: You’ll be working in a highly dynamic and collaborative environment, so we need someone who can communicate effectively and contribute across teams.
- Ownership and impact: Take full ownership of the AI solutions you develop, ensuring they are not only innovative but also scalable, maintainable, and aligned with real user workflows.
We can keep each as
- .
Requirements (mandatory):
From "What We're Looking For": bullet points:
- Strong engineering skills: Solid experience building production-grade, user-facing software systems. You’re a self-starter, capable of tackling complex engineering problems and making UI design decisions with minimal supervision.
- AI experience with a practical mindset: You’re familiar with AI technologies and frameworks, and you focus on delivering high-quality solutions that work in the real world, not just in theory.
- Quick iteration and experimentation: You’re comfortable releasing prototypes, collecting feedback, and iterating with a pragmatic mindset.
- Proven initiative: You take ownership and drive projects forward, pushing boundaries to find the most impactful solutions. You can deal with ambiguity and are able to define scope where things are loosely defined.
- Collaborative attitude: You communicate effectively with your peers. You’re open to feedback, and you bring a solutions-oriented mindset to the table.
Then "Requirements" list:
- Experience with LLMs, prompt engineering, and building applications powered by GenAI.
- Proven track record of delivering software that made it into production and is actively used by users.
- Exposure to working in cloud-native environments (e.g., AWS, GCP, Azure).
- Experience using observability tools to understand and troubleshoot system behavior.
We could combine these as requirements. Might be okay to have a single Requirements section with all these bullets.
Nice To Have list:
- Experience building or working with agent frameworks or multi‑agent workflows.
- Experience as a data analyst or work with data platforms (e.g., Looker, Tableau, PowerBI, Snowflake, DataBricks)
- Experience building tools for data engineering.
We'll make a Nice To Have section.
Benefits: from "Why You’ll Thrive At Grafana Labs" plus earlier benefits.
List:
- 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.
- Benefits include equity, bonus (if applicable) and other benefits listed here.
We could also
for prose. We must not include markdown fences. Just raw HTML. Let's craft. First, About the role: combine company description, initiative, role description. Could be multiple paragraphs but we can put in one
with multiple sentences. Responsibilities: list from "Key Responsibilities". We'll create
- each.
Requirements: combine "What We're Looking For" and "Requirements" and "Nice To Have"? Might be too much. Perhaps separate: Requirements (mandatory) and Nice To Have (optional). But headings allowed: Requirements, Skills. Could treat "What We're Looking For" as Skills/Qualifications. But we have both Requirements and Nice To Have. Let's create two sections: Requirements (mandatory) and Nice To Have (optional). However instruction says use headings like Skills etc. We'll include Requirements and Nice To Have as separate headings (
) if they have content. That's okay. Benefits: list from "Why You’ll Thrive At Grafana Labs" plus earlier benefits (equity, bonus). We'll create
- each benefit.
Pay:
with compensation range. We must not include boilerplate like EEO, privacy policy, etc. Let's extract specifics. About the role prose: "Grafana Labs is the company behind Grafana Cloud, the fully managed observability platform trusted by more than 10,000 organizations to ensure reliability, resolve incidents faster, and optimize telemetry at scale. Built on open source and open standards and designed for interoperability across any stack, Grafana Cloud brings AI to observability and observability to AI, giving teams (and their agents) unified visibility so they can see, understand, and act on all their disparate data, wherever it lives, and move at the speed of their ambitions. Customers, including Anthropic, Bloomberg, NVIDIA, Microsoft, and Salesforce, rely on Grafana Labs. We are a 100% remote company with 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. 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 opportunity and we are looking for candidates from the U.S. or Canada. Residents of Quebec are not eligible for this role. At Grafana Labs, we build observability tools that help users understand, respond to, and improve their systems – regardless of scale, complexity, or tech stack. We recently started a skunkworks initiative with a mission to bring observability to the rest of the business. Our goal is to make Grafana the single best place where humans and AI agents understand and act on real-time data from across the enterprise. As part of this initiative, we are building an AI-native data intelligence system that gives agents reliable, governed access to enterprise context. This system helps agents retrieve the right data, metadata, definitions, lineage, quality signals, and institutional knowledge without every agent builder having to maintain their own brittle context files. The principle is simple: the context system retrieves, agents decide, and data teams maintain the intelligence. The new team is a mix of seasoned Grafanistas and new hires. We operate with a high degree of autonomy and ownership, both as individuals and as a team. Engineers are empowered to make decisions, move quickly, and validate ideas early – while being supported by a deeply collaborative culture that values curiosity, feedback, and cross-functional partnership. We’re looking for a Staff-level Backend Engineer to help build the first production services for this context layer management system. You will design and ship the core backend architecture that powers ingestion, context storage, retrieval APIs, and agent-facing integrations. This is an early-stage role on a high-autonomy team, so you should be comfortable working through ambiguity, making pragmatic architectural decisions, and building systems that can evolve from internal dogfooding to production-grade SaaS. As the team matures, there’s a broad opportunity to expand or redefine this role based on impact and initiative." That's a lot but we can keep as prose. Responsibilities list: - Build the core backend services: Design, implement, test, and operate the first services for context ingestion, context indexing, retrieval orchestration, API access, source configuration, and system administration. - Create a scalable SaaS foundation: Help define and build the architecture for a multi-tenant service, including tenant isolation, usage tracking, quotas, audit logs, background jobs, and reliable service boundaries. - Power agent-facing retrieval workflows: Build APIs and service interfaces that allow AI agents, MCP tools, CLIs, and internal applications to retrieve relevant context, provenance, confidence signals, and warnings. - Work across product and infrastructure: Partner with the team to make practical tradeoffs between fast experimentation and long-term reliability, especially as the project moves from prototype to production. - Operate what you build: Instrument services with metrics, logs, traces, alerts, and dashboards. Use observability tools to understand system behavior and improve reliability. - Contribute to technical direction: Help shape the architecture, service boundaries, storage choices, API contracts, deployment patterns, and engineering practices for a new product area. - Effective communication: You’ll be working in a highly dynamic and collaborative environment, so we need someone who can communicate effectively and contribute across teams. - Ownership and impact: Take full ownership of the AI solutions you develop, ensuring they are not only innovative but also scalable, maintainable, and aligned with real user workflows. We can keep each as
- . Requirements (mandatory): From "What We're Looking For": bullet points: - Strong engineering skills: Solid experience building production-grade, user-facing software systems. You’re a self-starter, capable of tackling complex engineering problems and making UI design decisions with minimal supervision. - AI experience with a practical mindset: You’re familiar with AI technologies and frameworks, and you focus on delivering high-quality solutions that work in the real world, not just in theory. - Quick iteration and experimentation: You’re comfortable releasing prototypes, collecting feedback, and iterating with a pragmatic mindset. - Proven initiative: You take ownership and drive projects forward, pushing boundaries to find the most impactful solutions. You can deal with ambiguity and are able to define scope where things are loosely defined. - Collaborative attitude: You communicate effectively with your peers. You’re open to feedback, and you bring a solutions-oriented mindset to the table. Then "Requirements" list: - Experience with LLMs, prompt engineering, and building applications powered by GenAI. - Proven track record of delivering software that made it into production and is actively used by users. - Exposure to working in cloud-native environments (e.g., AWS, GCP, Azure). - Experience using observability tools to understand and troubleshoot system behavior. We could combine these as requirements. Might be okay to have a single Requirements section with all these bullets. Nice To Have list: - Experience building or working with agent frameworks or multi‑agent workflows. - Experience as a data analyst or work with data platforms (e.g., Looker, Tableau, PowerBI, Snowflake, DataBricks) - Experience building tools for data engineering. We'll make a Nice To Have section. Benefits: from "Why You’ll Thrive At Grafana Labs" plus earlier benefits. List: - 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. - Benefits include equity, bonus (if applicable) and other benefits listed here. We could also
- each benefit.
Pay: