Our client is an applied AI and data analytics company building the intelligence layer that powers enterprise decision-making. Its platform unifies an organization's full data landscape — internal systems, social media signals, industry reports, and consumer behavior data — into a single coherent intelligence layer that surfaces insights and automates workflows that previously consumed weeks of analyst time.

The core thesis is that research and data today remain fragmented: very little of it is connected, and enormous value disappears into that "dark data." Our client is converting sentiment from a lagging signal into a leading one, allowing brands to move months faster than legacy research tools permit. The platform is building toward a consumer ontology — think of Palantir's ontology, but for consumer intelligence — powered by a production graph RAG system that links temporal and sentiment data at a scale not previously attempted.

The product is already delivering eight-figure gross-margin improvements for Fortune 500 retailers. The go-to-market is a land-and-expand motion: starting in insights and research functions, then expanding into innovation, marketing, and eventually supply chain and manufacturing.

Founded by a technical team with deep experience in innovation and graph databases. $14M raised in seed funding. Emerging publicly after almost two years in stealth. Zero attrition since founding. The culture is genuine: weekly team activities (ping pong tournaments, Yankees games, happy hours, game nights), with plus-ones welcome at events.

This is a ground-floor opportunity — the engineers joining at this stage will have outsized influence on architecture, product direction, and culture.

This is a mid-level full-stack position. You will work across every layer of the platform — from backend services that process enterprise data at scale to frontend interfaces that make intelligence accessible and actionable. On a team this size, full-stack means full ownership: you will carry features from concept through production and iterate directly with enterprise customers.

The team operates on a DRI (Directly Responsible Individual) model — you own major product features end-to-end rather than simply contributing to them. This is a hands-on IC role for builders who ship.

⦁ Build and ship features end-to-end — backend services in Go/Python and frontend experiences in React/TypeScript

⦁ Design APIs, data models, and service architectures that support our client's agentic AI capabilities

⦁ Create intuitive interfaces that turn complex enterprise data into clear, actionable workflows

⦁ Partner with ML engineers to bring AI-driven features into production

⦁ Own features through the full lifecycle: scoping, architecture, implementation, testing, deployment, and iteration

⦁ Engage directly with enterprise customers and stakeholders to understand real-world needs and refine the product

⦁ Contribute to infrastructure, tooling, and developer experience as the engineering team grows

⦁ 3+ years of professional engineering experience with meaningful work spanning both frontend and backend

⦁ Proficient in TypeScript/React and at least one of: Go or Python

⦁ Strong product instincts — thinks about the user, not just the code

⦁ Experience with cloud infrastructure (AWS preferred) and modern deployment practices

⦁ Experience with agentic AI systems, LLM integrations, or RAG architectures

⦁ Background in enterprise SaaS, retail technology, or data-intensive products

⦁ Familiarity with data visualization, real-time systems, or streaming architectures

⦁ Contributions to developer tooling, CI/CD, or infrastructure automation

⦁ Graph database experience

Location: New York City — 4 days per week in office, with engineering generally able to take Fridays remote. Additional flexibility is considered case-by-case; the in-office norm is collaborative rather than a strict 5-day mandate.

Compensation: $140,000 – $170,000 base salary, plus equity (approximately 25% of base per year, vesting) and bonus.

Openings: Up to 2 hires

⦁ Medical, dental, vision, and 401(k)

⦁ Home office stipend and flexible PTO

⦁ Ground-floor equity at a well-funded seed-stage company

⦁ Strong team culture: weekly activities, team events, zero attrition to date

1. Recruiter screen

2. Introductory call with the team (culture and background fit)

3. Technical screen (45–60 minutes) with a senior engineer — architecturally focused, probing on background and hands-on ability (not pure coding, but candidates should demonstrate genuine fluency with code)

4. On-site (~4 hours) — coding interview, system design interview, product sense (30 minutes), AI sense (30 minutes), and a meeting with leadership. Note: a decision is often reached after the first two on-site interviews (~1 hour).

5. Offer