Founding AI Engineer

a16z-speedrun • San Francisco, CA

2d • Onsite

About The Position

Build the intelligent systems at the heart of an autonomous commercial real estate loan origination and processing platform. The platform aims to help lenders move loans from intake through underwriting with no manual work. You will work directly with the founders and customers and own the full production loop, including data, model selection and adaptation, evaluations, serving, monitoring, and iteration. The role turns messy, real-world financial information into reliable decisions and actions.

The company describes itself as an experienced team with alumni and tech leads from Meta, LinkedIn, and Rippling, backed by a16z through Speedrun, and working with multiple paid pilots. The team is moving quickly from customer conversations to production software.

The position is based in San Francisco and requires working in person with the team five days a week.

Requirements

• Evidence of having shipped ML systems used by real users.
• Strong software engineering fundamentals beyond notebooks and prototypes.
• Experience evaluating, adapting, and operating modern models in production.
• Sound judgment about model quality, failure modes, latency, and cost.
• Pragmatic approach: use the simplest approach that reliably solves the problem.
• Fluency with AI coding agents and ability to use them without outsourcing engineering judgment.
• Live in San Francisco and be available to work in person with the team five days a week.

Nice To Haves

• Experience with Python, PyTorch, model serving, LLMs, retrieval, document intelligence, or multimodal systems.
• Prior lending or commercial real estate experience is not required.

Responsibilities

• Build production AI systems for complex, high-stakes workflows.
• Develop models and pipelines for understanding unstructured and structured financial data.
• Design evaluation systems that measure quality, reliability, latency, and cost.
• Build feedback loops that turn customer usage into better models and products.
• Determine when to use foundation models, specialized models, deterministic systems, or a combination.
• Ship frequently, observe real-world performance, and iterate with customers.
• Establish the ML infrastructure and standards that future hires will inherit.
• Own the production loop across data, model selection and adaptation, evaluations, serving, monitoring, and iteration.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed