Founding AI Engineer

Rheaction•San Francisco, CA

7d•Onsite

About The Position

The Founding AI Engineer will help build and scale advanced AI-powered products at an early-stage startup. The role works across AI engineering, multimodal models, agentic systems, and product development, with significant ownership of technical architecture, model performance, and production systems. It is a hands-on role suited to an engineer who enjoys solving complex problems, experimenting with emerging AI technologies, and building products from the ground up. The position is in person in San Francisco, California, five days per week. It is full-time and offers compensation of $165,000–$210,000 plus 0.75%–2% equity. Candidates must already have U.S. work authorization without sponsorship.

Requirements

3–8 years of software engineering experience.
Strong proficiency in Python and solid software engineering fundamentals.
Hands-on experience developing AI-powered applications, agents, or LLM-based systems.
Experience with model evaluations, performance optimization, and experimentation.
Familiarity with multimodal AI, computer vision, or advanced LLM workflows.
Ability to design and own production-quality systems.
Experience using AI-assisted development tools such as Cursor or Claude Code.
High ownership, strong problem-solving ability, and comfort with ambiguity.
Ability to work independently in a fast-moving startup environment.
Existing U.S. work authorization without sponsorship.
Ability to work in person in San Francisco five days per week.

Nice To Haves

Experience with post-training, fine-tuning, or model optimization.
Experience building and evaluating multimodal or agentic AI systems.
Computer vision or visual reasoning application experience.
Experience building AI products from 0 to 1.
Previous founding engineer or early-stage startup experience.
Experience developing internal AI tooling and automated engineering workflows.

Responsibilities

Design, build, and optimize AI agents and multimodal AI workflows.
Improve model accuracy, reliability, and performance through evaluations, experimentation, and model optimization.
Develop and maintain evaluation frameworks to measure AI system quality.
Use LLMs, computer vision, and other AI technologies to solve complex problems.
Build supporting data pipelines, internal tools, and production infrastructure.
Contribute to backend engineering, product development, and AI infrastructure as needed.
Make architectural decisions and take ownership of core technical components.
Collaborate directly with users to understand problems and improve product capabilities.