AI Engineer - Enterprise

Hilbert's AI•San Francisco, CA

1d•Onsite

About The Position

Hilbert is building a reasoning engine that must navigate non-deterministic user behavior across data silos — turning months-long decision cycles into minutes. Fully agentic by design, our demand intelligence platform doesn't just call APIs; it solves the hard problem of orchestrating multi-step inference over messy, high-stakes enterprise data where deterministic answers don't exist. From Fortune 500 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 an AI Engineer who can build production-grade AI systems end-to-end and serve as the technical AI counterpart for our largest enterprise customers — understanding their workflows, translating their challenges into agentic solutions, and earning their trust through clarity, rigor, and results. All with the ownership and urgency of a startup culture. This is not a "wire up a prompt chain and move on" role. You'll own core pieces of the AI stack that power Hilbert's demand intelligence platform — designing agent architectures, building evaluation systems, and making hard tradeoffs between accuracy, latency, and cost in production. You'll also be the person our biggest customers look to when they want to understand what the AI is doing, why it made a particular decision, and how it can be shaped to solve their specific problems. If you think in systems, have opinions about how agentic workflows should actually work, can hold your own in a room full of enterprise stakeholders, and want to build AI products that drive real outcomes, we want to meet you. THE ROLE You'll work directly with the founding team and across product, data, and GTM to design, build, and improve the AI systems at the heart of Hilbert — with a particular focus on our largest enterprise accounts. You'll be hands-on every day — building agents, designing workflows, shipping to production — but you'll also be the technical AI voice in customer conversations: understanding their business context firsthand, shaping how we apply our agentic systems to their problems, presenting capabilities and results, and building the trust that turns a vendor relationship into a strategic partnership. 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. In this role, you're often the person closest to both the technology and the customer. What you'll do:

Requirements

You're a strong software engineer. Your code is clean, testable, and production-ready.
You have real experience with LangChain, LangGraph, or equivalent agent/orchestration frameworks. You've built with them, hit their limits, and worked around them — not just followed tutorials.
You're a trusted technical partner to enterprise stakeholders. You've been in the room with senior audiences and presented technical work in a way that earned trust and drove decisions. You're comfortable with hard questions, pushback, and the ambiguity of enterprise conversations. You don't oversell, you don't hide behind jargon, and you know how to make AI capabilities accessible without dumbing them down.
You're a product-minded engineer. You understand that a technically impressive agent is useless if it doesn't solve the customer's actual problem. You care as much about the why as the how — and your customer exposure keeps you grounded in what matters.
You communicate with clarity and conviction. You can explain a technical decision to a non-technical founder, debate architecture tradeoffs with a senior engineer, and walk an enterprise VP through an agentic workflow — all in the same day. Communication is not a nice-to-have here — it's the job.
You take ownership. You don't wait for tickets. You see what needs to be built, raise your hand, and ship it. If a customer isn't getting value or an integration isn't working, you treat it as your problem.
You thrive in ambiguity. AI products evolve fast. Customer priorities shift. Requirements change. You're energized by figuring it out — and you bring the customer along on the journey.
You move at startup speed. You understand what it means to be available, responsive, and biased toward action in a fast-moving, early-stage environment.

Nice To Haves

Experience in customer-facing technical roles — solutions engineering, applied AI, technical account management, or consulting where you owned the technical relationship
Experience building eval 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
Exposure to retrieval-augmented generation (RAG), vector databases, or LLM-powered search and recommendation systems
Deep exposure to retail, e-commerce, or enterprise B2C environments and the business teams that operate in them
Experience at early-stage startups or high-growth environments where you wore multiple hats

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 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
Shape the technical direction of the AI stack as the company scales
Be the technical AI counterpart for our largest accounts — understanding their workflows, data environment, and business challenges firsthand, and translating them into agentic solutions
Present AI capabilities, results, and roadmap to senior customer stakeholders with clarity, conviction, and appropriate nuance — you're the person they trust to explain what the system does and why
Translate customer context into engineering decisions — what you learn in customer conversations directly informs how you design agents, workflows, and integrations. You don't build in a vacuum; you build with deep knowledge of how the output will be used
Hold the line on what AI can and can't do — when customers want a simpler story than reality supports, or push for capabilities that aren't ready, you find a way to be honest and helpful at the same time. You build trust through intellectual integrity, not through overpromising
Design customer-specific configurations and integrations — enterprise customers have unique platforms, data flows, and operational requirements. You own the technical work of making our agentic systems fit their world, combined with human-in-the-loop elements that keep enterprise trust intact
Feed enterprise learnings back into the product — patterns you see across customers, gaps in our systems, new workflow opportunities. Your customer exposure makes the whole team smarter