Founding AI Engineer

Founding AI Engineer

Location

San Francisco, CA

On-site in San Francisco.

Company Description

Early-stage, VC-backed AI company building an AI-native commerce platform designed to consolidate merchant tools and workflows into a single intelligent interface.

The company is building a direct alternative to traditional commerce infrastructure, with an AI agent at the center of the product experience. The platform already has tens of thousands of users and is scaling rapidly.

This is a founding engineering opportunity where you will own the AI layer end-to-end — from models, prompting, retrieval, and tool use to agent orchestration, evaluation, and production infrastructure.

You will be one of the earliest engineers on the team, meaning the technical and architectural decisions you make early will have a long-term impact on the product.

This is a high-ownership, highly hands-on environment. You will work directly on production systems, ship frequently, and be responsible for understanding whether AI features actually deliver value for real users.

What You Will Do

1. Own End-to-End AI Systems

• Own the AI layer across prompting, retrieval, tool use, agent orchestration, and model behavior.
• Build production LLM systems that serve real customers.
• Design and improve AI pipelines from experimentation through production.
• Determine when to use prompting, retrieval, fine-tuning, or other approaches to solve product problems.
• Take ownership of AI systems from architecture through deployment and iteration.

2. Build Evaluation & Reliability Infrastructure

• Build evaluation harnesses that measure model and system quality before shipping changes.
• Create evaluation systems capable of catching regressions in production AI behavior.
• Establish metrics for model quality, reliability, latency, and cost.
• Continuously monitor AI systems and use real-world behavior to improve them.
• Develop practical evaluation frameworks rather than relying solely on qualitative testing.

3. Solve Ambiguous Product & AI Problems

• Take ambiguous product problems and determine whether the underlying issue is related to models, data, infrastructure, or user experience.
• Translate product requirements into practical AI system designs.
• Experiment quickly and validate solutions against real-world outcomes.
• Balance model quality with latency, cost, reliability, and merchant-scale requirements.
• Work across product and engineering to turn new AI capabilities into customer-facing features.

4. Ship & Scale Production AI

• Ship AI features to production on a frequent cadence.
• Build systems capable of operating reliably at increasing scale.
• Optimize inference, latency, and infrastructure costs.
• Debug model-shaped production issues and improve system reliability.
• Establish technical foundations that can support future AI engineers and product development.

Ideal Candidate Background

Experience Requirements

• 2+ years of hands-on AI engineering experience.
• Strong experience building production LLM systems.
• Has shipped an AI/ML-powered system to real production users — not only a research prototype or internal demo.
• Experience working at a company with a rigorous engineering bar.
• Startup or growth-stage VC-backed experience strongly preferred.
• Demonstrated ability to operate with high ownership and limited structure.
• Experience taking AI systems from experimentation through production.
• Strong candidates may come from AI engineering, ML engineering, applied AI, or highly technical software engineering backgrounds with significant AI ownership.

Technical Requirements

• Strong hands-on Python experience.
• Production experience building AI/ML services.
• Strong understanding of LLM-based systems.
• Experience with prompting, retrieval, tool use, and agent orchestration.
• Experience designing and building AI evaluation systems.
• Understanding of model quality, regressions, latency, and cost.
• Experience working with production APIs and backend systems.
• Familiarity with TypeScript, Node.js, and React is valuable given the broader product stack.

AI & Systems Requirements

• Strong applied AI focus rather than purely theoretical or research-oriented ML.
• Experience building LLM applications used by real customers.
• Experience building evaluation frameworks that identify model regressions.
• Experience with AI agents, tool calling, retrieval, or multi-step workflows.
• ML or fine-tuning experience is a strong plus.
• Ability to reason about whether a problem should be solved through models, data, infrastructure, or product design.
• Strong production engineering fundamentals around reliability, scalability, latency, and cost.
• Open-source projects, technical papers, or sophisticated side projects are strong additional signals.

Soft Skills

• Extremely high ownership and initiative.
• Comfortable working in an early-stage startup environment.
• Strong ability to operate with ambiguity.
• Product-minded and commercially aware.
• Comfortable making architectural decisions with incomplete information.
• Fast learner who can adapt as AI tooling and techniques evolve.
• Willing to be deeply hands-on rather than operating only at a strategic level.
• Strong communication and technical judgment.
• Comfortable working in a demanding, highly in-person environment.
• Willing to take responsibility for systems after they reach production.

Compensation & Benefits

• $200,000 – $400,000 base salary.
• 0.2% – 0.8% equity.
• Founding-level ownership and responsibility.
• Opportunity to shape the technical architecture of a rapidly scaling AI product.
• Direct impact on production AI systems and customer-facing features.
• High degree of autonomy and technical ownership.

Why Join

• Join as one of the earliest AI engineers and directly shape the company's AI architecture.
• Own the full AI stack rather than a narrow component of a larger ML organization.
• Work on production LLM systems with real users rather than research prototypes.
• Solve challenging problems across agents, evaluation, retrieval, model behavior, latency, and cost.
• Have meaningful influence over engineering standards and future AI hiring.
• Work closely with a small team where technical decisions have immediate product impact.

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