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# Senior/Staff Software Engineer, Agents

- **Company:** David Joseph & Company
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Salary:** $220,000.0 - $315,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Infrastructure, Distributed Systems, Software Product Management, Software Engineering, AI Infrastructure, Backend, Build Management, AI Platforms, Information Technology
- **Published:** October 30, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-staff-software-engineer-agents-san-francisco-ca--0557eaf4-3095-40fa-a3ae-0c33ffb37ac1

## About the Role

Software Engineering, Software Agents, Accounting, Consulting, Artificial Intelligence (AI), Business-to-Business (B2B) Software, Finance, Product Development, Financial Audit, Quality Management, * Currently operating at a Senior, Staff, or Tech Lead level (or higher); strong founders are also considered

 * Roughly 6-15 years of professional software engineering experience
 * One to two years spent building end-to-end agents or production AI platforms, including agentic loops, evals, and error analysis
 * Hands-on backend work on customer-facing agents that run in production at scale
 * Solid background in backend platform, data platform, or full-stack engineering
 * Computer science degree from a top-tier program (US or international)
 * Able to work hybrid from the San Francisco office, averaging about three days a week on-site

Nice to Haves

 * Time at a recently IPO'd tech company or an AI-native startup
 * Distributed systems experience

## Description

You'd join the team that owns the long-horizon agents at the core of the client's AI platform, with a primary focus on their Financial Audit product, the company's largest product line and its next growth engine. The work sits at the frontier of AI product development: agent knowledge, evaluation, and driving quality and reliability at scale. You'd take real ownership of how the team measures and improves agent quality, building on AI infrastructure that is already in place. The agents you ship are used by Big Four firms in production., * Set the evals strategy and error-analysis practice that defines how the team measures and improves agent quality

 * Design and build agent knowledge and evaluation infrastructure for long-horizon agents
 * Lead error analysis on agent behavior and turn findings into platform-level reliability fixes
 * Build and harden the backend systems that support agent execution, evaluation, and monitoring at scale
 * Help drive the platform's reliability and quality roadmap alongside the broader AI team
 * Mentor engineers and raise the bar on eval rigor across the team

Tech stack: Language-agnostic; the team builds with coding agents as part of its day-to-day workflow