Lead AI Engineer

Hilbert's AI•San Francisco, CA

1d•Onsite

About The Position

Hilbert is a scalable, data science-first growth engine that gives B2C teams predictive clarity into user behavior, revenue drivers, and the actions that drive sustainable growth. Fully agentic by design, Hilbert shrinks months-long decision cycles to minutes. From Fortune 10 enterprises to beloved brands like FreshDirect, Blank Street, and Levain Bakery, operators run their growth on Hilbert. We're also co-building alongside leading AI companies. We're looking for a Lead AI Engineer who can own the technical direction of Hilbert's AI stack, ship production-grade systems hands-on, and elevate a growing engineering team; all with the ownership and urgency of a founder. This is not a "manage from a distance" role. You'll write code, make architecture calls, set the bar for quality and velocity, and build the engineering culture as we scale. You're the person the team looks to when the problem is ambiguous, the stakes are high, and the path forward hasn't been written yet. If you combine deep technical craft with the ability to lead people and communicate with clarity, we want to meet you. You'll work directly with the founding team and across product, data, and GTM to lead the design, development, and evolution of the AI systems at the heart of Hilbert. You'll be hands-on and in the code daily — but you'll also be the person who defines how we build, how we prioritize, and how we grow the engineering team. The environment is high-autonomy and high-ambiguity — the nature of building AI-native products means requirements shift, approaches evolve, and the person closest to the problem often makes the call. As Lead, you make sure the team is equipped to make those calls well.

Requirements

You're a strong Python engineer first. Your code is clean, testable, and production-ready. You haven't left the codebase behind — you lead from inside it
You have deep experience with LangChain, LangGraph, or equivalent agent/orchestration frameworks. You've built with them at scale, hit their limits, designed around them, and have opinions about when to use them and when not to
You communicate with clarity and conviction. You can align a team around a technical direction, explain a tradeoff to a non-technical founder, and give direct, constructive feedback to an engineer — all in the same day. Communication is not a nice-to-have here — it's the job
You take ownership at the team level. You don't just own your own output — you own the team's output. If something falls through the cracks, you treat it as your problem
You thrive in ambiguity and help others do the same. AI products evolve fast. You bring structure to chaos without killing speed — and you coach the team to operate the same way
You move at startup speed and expect the same from your team. You understand what it means to be available, responsive, and biased toward action in a fast-moving, early-stage environment. You set that tempo

Nice To Haves

Experience building evals pipelines — designing metrics, running systematic evaluations, and using results to drive iteration on AI systems
Backend software engineering experience — building APIs, services, data infrastructure, or production systems beyond the ML/AI layer
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 — not just managing them
A senior engineer or tech lead at a startup who's ready to own the entire AI function. A founding engineer who built a team around themselves and wants to do it again at a company where the AI stack is the product. An engineering manager who refuses to stop coding. Someone who's been leading agents and LLM infrastructure work at a larger company and wants full ownership and zero bureaucracy.

Responsibilities

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
Define and own the technical roadmap for the AI stack in partnership with the founding team
Make architecture and infrastructure decisions that balance speed today with scalability tomorrow
Set engineering standards — code quality, review practices, testing, documentation, and deployment discipline
Prioritize ruthlessly across competing demands, keeping the team focused on highest-impact work
Communicate technical strategy, tradeoffs, and progress clearly to founders and non-technical stakeholders
Be the tiebreaker when the team is stuck — on architecture, approach, or prioritization
Hire, mentor, and develop AI engineers as the team scales
Create an environment of ownership, intellectual honesty, and high-velocity shipping
Run effective processes without bureaucracy — standups, reviews, retros that actually help
Identify skill gaps and build the team to fill them — whether through hiring, upskilling, or restructuring work
Lead by example: the team sees you in the code, in the reviews, in the hard problems — not just in meetings