About The Position

Commerce is undergoing a fundamental shift. For the past decade, ecommerce has been built around static pages, manual optimization, and fragmented tooling: from analytics to experimentation to personalization. Newco is building the AI-native intelligence layer for DTC commerce. Our platform interprets shopper behavior from the moment of ad click, scores intent in real time, and dynamically personalizes the on-site experience without requiring brands to rebuild their infrastructure. Every interaction compounds: the system learns, adapts, and improves outcomes continuously. The result: higher conversion, stronger retention, and a lasting edge for brands competing in a signal-scarce, AI-native world.

Requirements

  • 5 to 7+ years of full-stack engineering experience
  • Experience building real-time systems, data pipelines, or decisioning / recommendation engines
  • Familiarity with LLM agent development and frameworks (LangGraph, PydanticAI, or similar)
  • Production-level experience with FastAPI, Docker, and cloud-native deployment
  • Comfort with vector databases and retrieval-augmented generation patterns
  • Proficiency in agentic IDEs (Cursor, Windsurf, Claude Code)
  • A builder-leader at the intersection of AI, product, and real-time systems
  • Experienced in high-stakes, fast-moving environments: vertical SaaS, applied ML, or personalization/recommendation systems
  • Comfortable working across backend, frontend, and product, not siloed
  • Strong product intuition alongside technical execution
  • Thrives in ambiguity and early-stage building

Nice To Haves

  • Experience in ecommerce, marketplaces, or personalization platforms
  • Familiarity with event-driven or streaming architectures (Kafka, Kinesis)
  • Shopify ecosystem knowledge

Responsibilities

  • Define system architecture from first principles and own it end-to-end
  • Build the initial product hands-on (this is a 0 to 1 role)
  • Design and scale the decisioning and personalization engine across data, infra, and product layers
  • Work directly with the founder on product direction and technical strategy
  • Hire and lead the early engineering team as the company grows
  • Communicate technical tradeoffs and system design to non-technical stakeholders in outcome-oriented terms
  • Decisioning and Personalization Architecture: Design and scale the real-time scoring and routing engine: interpreting incomplete behavioral signals, making decisions under uncertainty, and serving personalized experiences with low latency.
  • LLM and Agentic Systems: Build and maintain AI agent layers using frameworks like LangGraph or PydanticAI, including modular reasoning pipelines (RAG, ReAct, planner-executor) for CRO automation and content decisioning.
  • Data Infrastructure: Integrate event-driven pipelines, vector databases (pgvector, Pinecone), and structured data stores to support real-time retrieval, persistent memory, and behavioral pattern recognition.
  • Evaluation and Observability: Build tools for trace inspection, confidence scoring, and output validation. Develop evaluation loops and test harnesses to ensure reliability and consistency across decisioning outputs.
  • Engineering Architecture and Delivery: Define technical standards, development processes, and system architecture to support a scalable, maintainable product. Partner with product leadership to translate strategy into roadmap.

Benefits

  • Significant equity + competitive cash
  • True technical ownership: define the system architecture from day one
  • Backed environment: venture studio with capital, operational support, and design partners already in market
  • 0 to 1 opportunity: build a company, not just write code
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