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The role involves designing and delivering integrations across the GTM stack, developing internal tools, and leading technical architecture in a collaborative setting.

The Senior Software Engineer will develop AI-driven internal tools for automation across Sales, Customer Success, and Marketing, ensuring scalable workflows and system integrations with various platforms.

Lead backend development of Pyroscope by designing, building, operating, and maintaining distributed systems for profile ingestion and query. Own projects end-to-end, participate in on-call rotations, mentor teammates, collaborate with customers and Grafana Cloud, and improve reliability, performance, and availability.

Build and run scalable AI-first GTM automation: design multi-agent architectures, integrate LLM APIs and marketing/CRM platforms, implement CI/CD and observability, and enable partner teams with templates, docs, and training to drive measurable GTM outcomes.

Design, build, and operate production multi-agent AI systems and automation for Marketing Operations. Integrate LLMs with business systems (BigQuery, CRMs, Slack), implement RAG pipelines, observability, governance, and scalable serverless/containerized services. Partner with GTM teams to deliver measurable automation and self-service tooling.

Embed with a core business area to define problems, set metrics, run analyses and experiments, build reports, and surface actionable insights. Drive outcomes by recommending and implementing data-driven decisions across growth, product, AI/automation, or partnerships, and communicate findings to stakeholders and leadership.

Embedded within a core business area, you will define analytical problems, build metrics and reports, run analyses and experiments, surface insights, and drive data-driven recommendations end-to-end to influence product, growth, AI, or partnerships.

Embed within a core business area to own analytics end-to-end: define problems, build metrics/reports/experiments, analyze data, surface insights, and drive outcome-focused recommendations with stakeholders to improve growth, product, AI/automation, or partnerships.

Embed on a core business team to define problems, build metrics, run analyses and experiments, and drive data-informed product, growth, AI, or partnership decisions end-to-end. Surface insights, recommend actions, and own execution to improve key business metrics.

Embed with a business unit to define problems, build metrics and analyses, run experiments, surface insights, and drive data-informed decisions end-to-end across growth, product, AI/automation, or partnerships.

Embedded on a core business team, the Data Scientist defines problems, builds metrics and reports, runs analyses and experiments, and translates insights into executable recommendations to drive growth, product, AI/automation, or partnership outcomes.

Embed within a business unit to define problems, build metrics/reports/experiments, surface insights, and drive data-informed decisions end-to-end across growth, product, AI/automation, or partnerships.

Embedded on a core business team, you will define problems, build metrics/reports, run analyses and experiments, and drive data-driven recommendations end-to-end. You will partner with product, growth, finance, and operations to surface insights and influence leadership decisions.

The Jr. Firmware Engineer will execute Firmware testing, manage test automation frameworks, and collaborate with cross-functional teams to ensure quality and timely product releases.

Own the analytics function for an assigned business area (growth, product, AI, or partnerships). Define problems, metrics, and experiments; pull and analyze data; build reports and dashboards; surface insights and drive recommendations through to execution to improve key business metrics.

Embedded on a core business team, you will define problems, build metrics and reports, run analyses and experiments, surface insights, and drive cross-functional decisions end-to-end to improve growth, product, AI/automation, or partnerships.

Embed with a core business area to define problems, build reports and experiments, analyze data, surface insights, and drive decisions end-to-end to improve growth, product, AI/automation, or partnerships.

Embedded BizOps analytics leader owning end-to-end analytics for a business unit: define problems, build reports and experiments, analyze data, surface insights, drive recommendations and execution across growth, product, AI/automation, or partnerships to improve key metrics and scale the business.

Embed with a core business unit to define problems, build metrics and reports, run analyses and experiments, surface insights, and drive data-informed decisions end-to-end across growth, product, AI, or partnerships.

Own analytics for an embedded business unit (growth, product, AI/automation, or partnerships): define problems, build metrics and reports, run analyses and experiments, surface insights, make recommendations, and drive execution with cross-functional stakeholders to move key business metrics.

Own analytics for an embedded business unit (growth, product, AI, or partnerships). Define problems, build metrics and reports, run analyses and experiments, surface insights, make recommendations, and drive outcomes end-to-end.

Embed with a business unit to define problems, build metrics/reports/experiments, analyze data, surface insights, and drive recommendations to improve product, growth, AI, or partnerships.

Design and build authentication, authorization, and audit platform services (RBAC/ReBAC, token issuance, session handling), implement SAML/SCIM/OIDC/OAuth2/MFA flows, ship SDKs and APIs, operate distributed systems at scale, build audit telemetry, and support migrations and on-call production reliability.

The AI Engineer will design and deploy machine learning models and AI agents to optimize logistics workflows, collaborating with cross-functional teams to integrate solutions.

Build and operate core backend services, APIs, and platform components for enterprise AI; integrate LLMs, retrieval and agent workflows; ensure reliability, observability, testing, and scalability; collaborate with research, product, and design to productionize AI capabilities.