About Our Client
Our client is an applied 麻豆传媒映画 and data analytics company building the intelligence layer for enterprise decision-making. Their platform unifies an organization鈥檚 full data landscape 鈥� internal systems, social media signals, industry reports, and consumer behavior data 鈥� 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The core thesis: research and data today are stuck in an outdated state 鈥� very little of it is connected, and enormous value is lost in that dark data.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鈥檚 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鈥檛 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 鈥� 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 鈥� engineers joining at this stage will have outsized influence on architecture, product direction, and culture.
About the Role
This is our client鈥檚 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:
1.鈥傾rchitecture and scale 鈥� ensuring the platform can sustain significantly higher usage as enterprise onboarding ramps up.
2.鈥傿uilding 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 鈥� 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.
Key Responsibilities
猞佲�僁esign, build, and scale backend services in Go and Python that power autonomous intelligence for enterprise clients.
猞佲�傾rchitect data ingestion and processing pipelines that handle millions of data points across internal and external sources.
猞佲�傿uild and maintain APIs that power the agentic 麻豆传媒映画 platform, supporting real-time decision-making at enterprise scale.
猞佲�僕ork on the graph database layer and production graph RAG system 鈥� a core technical differentiator.
猞佲�働artner with ML/麻豆传媒映画 engineers to integrate predictive and prescriptive models into production workflows.
猞佲�僌wn system reliability 鈥� design for fault tolerance, observability, and performance under high-throughput conditions.
猞佲�僀ontribute to architectural decisions that shape the long-term trajectory of the platform.
猞佲�僊entor engineers and help establish backend engineering best practices as the team scales.
Requirements
猞佲��5+ years of professional backend engineering experience, with meaningful time spent building systems at scale.
猞佲�僑trong Python proficiency is required 鈥� with the confidence that picking up Go would be a non-issue (Go experience is a plus but not required).
猞佲�僂xperience with distributed systems and microservices architecture.
猞佲�僂xperience with both SQL and NoSQL databases; comfortable handling data processing at scale.
猞佲�僑tartup or small-team experience strongly preferred 鈥� candidates who have operated with real ownership and built new systems, rather than only maintained existing ones.
Bonus Skills
猞佲�僂xperience with graph databases (major plus 鈥� core to our client鈥檚 stack).
猞佲�僂xperience with Go.
猞佲�僃amiliarity with RAG architectures and the broader Gen麻豆传媒映画 landscape.
猞佲�僂xperience with real-time data processing, streaming technologies, and concurrency at scale.
猞佲�僓nderstanding of ML/麻豆传媒映画 concepts, particularly in forecasting and NLP.
猞佲�僈ubernetes and container orchestration experience.
Logistics
Location
New York City 鈥� 4 days/week in office, with engineering typically taking Fridays flexible/remote. Additional case-by-case flexibility is available; it鈥檚 an in-person culture with understanding, not a strict 5-day rule.
Compensation
$160,000 鈥� $240,000 base + equity (~25% of salary per year, vesting) + bonus.
Openings
Up to 2 鈥� 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
猞佲�僅ealth, dental, vision, and 401(k).
猞佲�僅ome office stipend and flexible PTO.
猞佲�僄round-floor equity at a well-funded seed-stage company.
猞佲�僑trong team culture: weekly activities, team events, and zero attrition to date.
Interview Process
1.鈥僐ecruiter screen.
2.鈥僆ntro call with the team (culture and background fit).
3.鈥僒echnical screen (45鈥�60 min) with a senior engineer 鈥� 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.)
4.鈥僌n-site (4 hours) 鈥� Coding interview, system design interview, product sense (30 min), 麻豆传媒映画 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.
5.鈥僌蹿蹿别谤.