Founding Backend Engineer - Remote, Equity, Early-Stage

Rise Talent Advisors; Austin (TX); Remote; USD 120,000 - 190,000; Full time

Benefits offered by this job

Equity

Job summary

Rise Talent Advisors is helping an early stage, venture backed AI company hire founding engineers. The company builds AI agents that take slow, repetitive document work off the plates of large professional services firms, so reviewers can spend their time on judgment.

Its core engine reads long, messy source documents end to end, pulls out the data, and puts it into the firm's existing workpapers and filing tools. Customer data stays in the United States and is not used to train models.

Qualifications

• 2–6 years as a software engineer, hands-on contributor.
• Experience at early-stage, venture-backed startup or founding engineer.
• Proficiency in Python and TypeScript.
• Led backend architecture decisions for systems with LLM features.
• Shipped large language model or agent-based features to production.
• Ownership of features from design to production.
• CS degree from a top US university with GPA.
• US citizenship or permanent residency; no visa sponsorship.
• Builder mindset with ownership and urgency.

Responsibilities

• Build the core platform as a generalist, backend with some frontend work.
• Make backend architecture decisions with founders and leadership.
• Design and build document extraction and LLM agent pipelines.
• Ship the next major version and build related products.
• Own features from design to production and handle production failures.
• Translate customer needs into product features.
• Set early engineering practices, tooling, and standards.
• Move with urgency on a small, high-ownership team.

Skills

Python

TypeScript

Backend architecture

LLM features

Production experience

Founding engineer

Education

CS degree from top US university

Job description

Rise Talent Advisors is helping an early stage, venture backed AI company hire founding engineers. The company builds AI agents that take slow, repetitive document work off the plates of large professional services firms, so reviewers can spend their time on judgment.

Its core engine reads long, messy source documents end to end, pulls out the data, and puts it into the firm's existing workpapers and filing tools. Customer data stays in the United States and is not used to train models.