AI Engineer

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 Fresh Direct, 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 – from prototype to pipeline to product – with the ownership and urgency of a startup culture.

This is not a “fine‑tune a model and hand it off” role. You’ll own core pieces of the AI stack that power Hilbert’s demand intelligence platform, ship fast in conditions where the spec is evolving, and communicate what you’re building (and why) with clarity to the rest of the team. If you write clean Python, think in systems, and want to build AI products that drive real enterprise outcomes, we want to meet you.

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. 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.

• Design, build, and maintain AI‑driven features and pipelines that serve enterprise customers at scale
• Architect and implement agent‑based workflows using Lang Chain, Lang Graph, 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
• Collaborate closely with the founding team and cross‑functional partners – communicating tradeoffs, progress, and technical decisions with clarity
• Make pragmatic engineering decisions under ambiguity – ship, learn, iterate
• Shape the technical direction of the AI stack as the company scales

We care about how you think and how you ship – not how many years are on your resume.

• You’re a strong Python engineer. Your code is clean, testable, and production‑ready.
• You have real experience with Lang Chain, Lang Graph, or equivalent agent/orchestration frameworks. You’ve built with them, hit their limits, and worked around them – not just followed tutorials.
• You communicate with clarity and conviction. You can explain a technical decision to a non‑technical founder and debate architecture tradeoffs with a senior engineer. Communication is not a nice‑to‑have here – it’s core to the role.
• You take ownership. You don’t wait for tickets. You see what needs to be built, raise your hand, and ship it.
• You thrive in ambiguity. AI products evolve fast. Requirements change. You’re energized by figuring it out.
• 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.

• 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.
• Experience at early‑stage startups or high‑growth environments where you wore multiple hats.

A backend engineer who went deep on LLMs and never looked back. An ML engineer who realized they love building products, not just models. A startup CTO who wants to go deep on AI at a company where the stack is the product. Someone who’s been hacking on agents and pipelines nights and weekends and wants to do it full‑time with real enterprise stakes.

What matters: you ship, you own it, and you communicate like a teammate – not a silo.

San Francisco, with occasional travel for team meets, off‑sites, or customer engagements.

Competitive salary + equity package, commensurate with experience. Performance‑based bonuses tied to project milestones and customer impact.

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

Fast, human, no bureaucracy.