Lead AI Engineer

Role

Lead the design, development, and evolution of Hilbert’s AI systems. Own the AI stack end-to-end, ship production-grade agentic features and pipelines, set technical direction in partnership with founders, and grow the engineering team while remaining hands-on in the code.

Key Responsibilities

• Build — hands-on, every day

  • Design, build, and maintain AI-driven features and pipelines that serve enterprise customers at scale
  • Architect and implement agent-based workflows using LangChain, LangGraph, or equivalent orchestration frameworks
  • Own critical systems end-to-end from experimentation through production deployment and monitoring
  • Build and improve evaluation pipelines to measure, validate, and iterate on AI system performance
  • Make pragmatic engineering decisions under ambiguity — ship, learn, iterate

• Lead — set direction and raise the bar

  • Define and own the technical roadmap for the AI stack
  • Make architecture and infrastructure decisions balancing short-term speed and long-term scalability
  • Establish engineering standards for code quality, reviews, testing, documentation, and deployment discipline
  • Prioritize ruthlessly and communicate technical strategy and tradeoffs to technical and non-technical stakeholders

• Grow — build the team and the culture

  • Hire, mentor, and develop AI engineers as the team scales
  • Create an environment of ownership, intellectual honesty, and high-velocity shipping
  • Run effective processes (standups, reviews, retros) without bureaucracy

Requirements

• Strong Python engineer with clean, testable, production-ready code and an active coding practice
• Deep experience with LangChain, LangGraph, or equivalent agent/orchestration frameworks at scale
• Proven ability to own AI systems end-to-end, including deployment, monitoring, and evaluation
• Product-minded engineering leadership: align technical direction with business outcomes
• Excellent communication skills and experience aligning teams and explaining tradeoffs to non-technical stakeholders
• Comfortable operating in high-autonomy, high-ambiguity startup environments and setting tempo for the team

Strong Pluses

• Experience building evaluation pipelines and defining metrics that predict real-world performance
• Backend software engineering experience (APIs, services, data infrastructure, production systems)
• Exposure to retrieval-augmented generation (RAG), vector databases, or LLM-powered search and recommendation systems
• Prior experience as a tech lead, engineering manager, or founding engineer at an early-stage or high-growth company
• Track record of hiring and developing engineers

Current Hurdles

• Defining intelligent retrieval across heterogeneous approaches (RAG, vector DBs, graph-based retrieval)
• Designing agentic workflows that handle edge cases, missing data, escalation to humans, and graceful degradation
• Building systematic, reproducible evaluation processes that predict real-world performance
• Architecting integration layers for agents to take action and execute workflows with human-in-the-loop checkpoints

Location & Compensation

• Location: San Francisco, US
• Compensation: Competitive salary + equity; specific compensation details shared in later steps

Hiring Process

Short form intro call → Technical working session → Team conversations → Offer