About the Company

An applied AI and data analytics company building the intelligence layer for enterprise decision-making. The platform connects an organization's entire data landscape — internal systems, social media trends, industry reports, consumer behavior signals — into a single coherent intelligence layer that surfaces insights and automates workflows that used to take analysts weeks.

The core thesis: research and data are stuck in an outdated state — very little of it is connected, and massive value gets lost in that gap. The company is flipping sentiment from a lagging indicator into a leading indicator, helping brands make decisions months faster than legacy research tools allow. The platform is building toward a consumer intelligence ontology — powered by a production graph RAG system, connecting dots across temporal and sentiment data at a scale unlike anything built before.

The platform drives 8-figure improvements in gross margins for Fortune 500 retailers, with a land-and-expand strategy starting in insights/research departments and expanding into innovation, marketing, supply chain, and manufacturing.

Founded by a technical team with deep innovation and graph database backgrounds. Raised $14M in seed funding. Just launching publicly after nearly two years in stealth. Zero attrition — no one has left the company. Strong culture: weekly team activities, happy hours, game nights. This is a ground-floor opportunity — engineers joining now will have outsized influence on architecture, product direction, and culture.

11–50 employees | Founded 2024 | Validated with Fortune 500 brands during a 2-year stealth phase | Serves Fortune 100 brands

About the Role

This is the highest-priority hire on the team right now. There's currently one staff backend engineer working across large parts of the platform, and the goal is to bring in additional senior backend capacity. The focus is on two things: (1) architecture and scale — making sure the platform can sustain significantly increased usage as enterprise onboarding ramps up, and (2) building innovative new features with architecturally sound decisions that hold up for years, not months.

This means working across the graph database layer, API infrastructure, data pipelines with concurrency at scale, and new feature development. This is a DRI (Directly Responsible Individual) role — hands-on senior IC who owns systems end-to-end.

The sweet spot: someone senior enough to think about architecture at a high level, but still excited about actual implementation — not someone who's moved past hands-on work.

What You'll Own

• Design, build, and scale backend services using Go and Python that power autonomous intelligence for enterprise clients
• Architect data ingestion and processing pipelines handling millions of data points across internal and external sources
• Build and maintain APIs serving the agentic AI platform, supporting real-time decision-making at enterprise scale
• Work with the graph database layer and production graph RAG system — a core technical differentiator
• Collaborate with ML/AI engineers to integrate predictive and prescriptive models into production workflows
• Own system reliability — fault tolerance, observability, performance under high-throughput conditions
• Contribute to architectural decisions shaping the platform's long-term trajectory
• Mentor engineers and establish backend engineering best practices as the team scales

What We're Looking For

Must Have

• 5+ years of professional backend engineering experience, with meaningful time spent building systems at scale
• Strong Python proficiency — confident picking up Go if not already known (Go is a plus, not required)
• Experience with distributed systems and microservices architecture
• Comfortable with both SQL and NoSQL databases, data processing at scale
• Startup or small-team experience strongly preferred — real ownership, building new things, not just maintaining existing systems

Nice to Have

• Graph database experience — major plus, core to the company's stack
• Experience with Go
• Familiarity with RAG architectures and the broader GenAI landscape
• Experience with real-time data processing, streaming technologies, and concurrency at scale
• Understanding of ML/AI concepts, particularly forecasting and NLP
• Kubernetes and container orchestration experience