Senior Backend Engineer

Our client is an applied AI and data analytics company building the intelligence layer for enterprise decision-making. Their 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 took analysts weeks to complete. The core thesis: research and data today are stuck in an outdated state — very little of it is connected, and enormous value is lost in that dark data. Our client is turning sentiment from a lagging indicator into a leading one, allowing brands to make decisions months earlier than legacy research tools allow. The platform is being built toward a consumer ontology (comparable in spirit to Palantir’s ontology, but for consumer intelligence), powered by a production graph RAG system that connects signals across temporal and sentiment data at a scale that hasn’t been attempted before. The platform is driving 8-figure gross-margin improvements for Fortune 500 retailers. The go-to-market is a classic land-and-expand: starting in insights and research, then expanding into innovation, marketing, and eventually supply chain and manufacturing. Founded by a technical team with deep backgrounds in innovation and graph databases. $14M raised in seed funding. The company is launching publicly after nearly two years in stealth. Zero attrition to date — no one has left. The culture is strong: weekly team activities (ping pong tournaments, Yankees games, happy hours, game nights), with plus-ones welcome at events. This is a ground-floor opportunity — engineers joining at this stage will have outsized influence on architecture, product direction, and culture. About the Role This is our client’s highest-priority hire. A staff backend engineer is currently working across large portions of the platform, and the goal is to add additional senior backend capacity. The focus is on two things: Architecture and scale — ensuring the platform can sustain significantly higher usage as enterprise onboarding ramps up. Building innovative new features on the roadmap with architecturally sound decisions that hold up for years, not just months. In practice, this means working across the graph database layer, API infrastructure, data pipelines with concurrency at scale, and net-new feature development. This is a DRI (Directly Responsible Individual) role — a hands-on senior IC who can own systems end-to-end. The sweet spot: someone senior enough to think about architecture at a high level, but who still has boundless energy and excitement for actual implementation. Not someone who has moved past hands-on work. Responsibilities Design, build, and scale backend services in Go and Python that power autonomous intelligence for enterprise clients. Architect data ingestion and processing pipelines that handle millions of data points across internal and external sources. Build and maintain APIs that power the agentic AI platform, supporting real-time decision-making at enterprise scale. Work on the graph database layer and production graph RAG system — a core technical differentiator. Partner with ML/AI engineers to integrate predictive and prescriptive models into production workflows. Own system reliability — design for fault tolerance, observability, and performance under high-throughput conditions. Contribute to architectural decisions that shape the long-term trajectory of the platform. Mentor engineers and help establish backend engineering best practices as the team scales. Qualifications 5+ years of professional backend engineering experience, with meaningful time spent building systems at scale. Strong Python proficiency is required — with the confidence that picking up Go would be a non-issue (Go experience is a plus but not required). Experience with distributed systems and microservices architecture. Experience with both SQL and NoSQL databases; comfortable handling data processing at scale. Startup or small-team experience strongly preferred — candidates who have operated with real ownership and built new systems, rather than only maintained existing ones. Preferred Skills Experience with graph databases (major plus — core to our client’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 in forecasting and NLP. Kubernetes and container orchestration experience. Pay range and compensation package $160,000 – $240,000 base + equity (~25% of salary per year, vesting) + bonus. Equal Opportunity Statement Our client is committed to diversity and inclusivity in the workplace. Logistics Location: New York City — 4 days/week in office, with engineering typically taking Fridays flexible/remote. Additional case-by-case flexibility is available; it’s an in-person culture with understanding, not a strict 5-day rule. Openings Up to 2 — likely 1 senior plus 1 mid-to-senior. Strong candidates who are not fully senior may be considered at a slightly lower level or routed to a full-stack role. Benefits / Other Health, 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, and zero attrition to date. Interview Process Intro call with the team (culture and background fit). Technical screen (45–60 min) with a senior engineer — architecturally focused, probing into background. Not a pure coding interview, but designed to evaluate whether the candidate can speak credibly to items on their resume and reason architecturally. (The team is considering adding more hands-on coding probes to this stage based on recent candidate quality.) On-site (4 hours) — Coding interview, system design interview, product sense (30 min), AI sense (30 min), and a meeting with the co-founder and another leader. Note: decisions are often made after the first two on-site interviews (~1 hour), as many candidates have not made it through the full day. #J-18808-Ljbffr