Software Engineer, Agents (Foundation Agents)

4 нед. назад

190k–220k USD / годСШАMiddleHybrid

llm apierror analysisbackend systemsevaluation infrastructure

Develop and maintain backend systems for AI long-horizon agents, focusing on knowledge, evaluation, and quality improvements.

is establishing a new state of trust for global commerce and capital markets by automating and streamlining the work of assurance and audit practitioners—specifically in cybersecurity, privacy, and financial audits. We build software for the people who enable trust between businesses.
We're based in San Francisco, CA, and backed by Goldman Sachs Alternatives, Bessemer Venture Partners, 8VC, Floodgate, Y Combinator, and more. Over 50 of the top 100 accounting and consulting firms trust to power mission-critical work.
Fearless — Inspire and break down seemingly impossible walls
Fast — Launch fast with excellence; iterate to perfection
Lovable — Deliver happiness and 11-star experiences
Owners — Execute and run the business with ownership
Win-win — Create mutual value and earn trust for life
Inclusive — Scale the best ideas with inclusive teams

The Foundation Agents team stewards the long-horizon agents powering the AI platform. We work at the frontier of AI product development: agent knowledge, evaluations, and improving quality and reliability at scale. If you're excited by long-horizon agents that do real-world work, this is the team for you.
Build and maintain agent knowledge and evaluation infrastructure for 's long-horizon agents
Build our next generation of agents at , solving increasingly complex customer use cases
Perform error analysis on agent behavior and help turn findings into concrete quality improvements
Build backend systems that support agent execution, evaluation, and monitoring
Partner with senior engineers on the team to execute the platform's reliability and quality roadmap

You've built AI products, not just called an LLM API
You're curious about evals and error analysis. You want to understand why an agent failed, not just that it did
You're comfortable in the backend, with a bias toward reliable systems
You're motivated by long-horizon agents that do real work in production
Hands-on experience building AI products
Working knowledge of evals and error analysis
Backend engineering experience

Long-horizon agents: working on agents that do real, sustained work and not one-shot demos
Evaluation as a craft: Evals and error analysis are core to how this team improves quality
Platform-level impact: Your work shapes the reliability and quality of every agent built on top of it
Frontier problems: You're working on open problems in agent reliability that don't yet have established playbooks