Founding AI Infrastructure Engineer (On-Site)

Fuel Talent LLC; San Francisco (CA); On-site; USD 170,000 - 250,000; Full time

Job summary

Fuel Talent LLC in San Francisco seeks a Founding Engineer to build core infrastructure powering RL training environments and agent workflows. You will work directly with the founders, customers, and domain experts to translate real-world workflows into production-ready systems.

The ideal candidate thrives in ambiguity, ships quickly, and takes ownership—from idea to production—defining both technical direction and the operating model for an early-stage AI company.

Qualifications

• 2–5 years of backend or fullstack engineering in fast-paced environments.
• Strong proficiency in Python.
• Experience building production systems (APIs, backend services, data workflows, infrastructure tooling).
• Experience at VC-backed startups OR 1–2 years at a high-caliber big tech company.
• Strong communicator able to collaborate across engineering, operations, and customers.
• Undergraduate degree in Computer Science or related technical field.

Responsibilities

• Own and deliver core product systems and infrastructure end-to-end.
• Design and build RL training environments and supporting backend systems.
• Translate customer workflows into structured environments for agent training.
• Partner with founders to define technical priorities, architecture, and execution strategy.
• Build scalable internal tools to support expert labelers and domain specialists.
• Contribute to product direction through rapid iteration and experimentation.
• Operate with a product mindset, balancing speed, quality, and usability.
• Make foundational decisions that shape long-term platform architecture.

Skills

Python

Backend development

APIs / backend services

Data workflows

Education

Bachelor's degree in Computer Science or related field

Job description

Fuel Talent LLC in San Francisco seeks a Founding Engineer to build core infrastructure powering RL training environments and agent workflows. You will work directly with the founders, customers, and domain experts to translate real-world workflows into production-ready systems.

The ideal candidate thrives in ambiguity, ships quickly, and takes ownership—from idea to production—defining both technical direction and the operating model for an early-stage AI company.