Brex
Software Engineer, Forward Deployed Agent Builder
Seattle, Washington, United States$152k–$240kmidAdded 1 month ago
About this role
Brex seeks a forward-deployed AI engineer to build and ship intelligent agents that automate internal workflows across the company. You'll partner with teams to understand their processes, then design and deploy agentic systems using LLMs and modern frameworks, measuring impact through defined metrics and feedback loops.
What you'll do
- Embed with internal teams to deeply understand workflows and job functions through shadowing and collaboration
- Design and deploy AI agents that automate real workflows across different organizational functions
- Integrate agents with internal systems, APIs, and data sources to enable end-to-end automation
- Define evaluation frameworks, success metrics, and feedback mechanisms to measure agent performance
- Build shared tooling, playbooks, and frameworks that accelerate future agent deployments
- Own reliability, adoption, and business impact of deployed solutions
What they're looking for
- LLM and agent framework development (e.g., MCP, function calling, RAG)
- SQL and NoSQL database design and optimization
- API integration and system architecture
- Workflow decomposition and automation design
- Cross-functional collaboration and stakeholder management
- Full-stack AI/automation system development
- Production-grade AI systems shipping
- Data modeling and query performance tuning
Benefits
- Hybrid work environment with 3 coordinated office days per week (Mon/Wed/Thu)
- Up to four weeks of fully remote work annually
- Competitive salary range: $152,000 - $240,000
- Equity compensation as part of total package
- Collaborative environment with high autonomy
- Opportunity to work with world-class companies and cutting-edge AI technology
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Brex
Brex builds mission-critical financial infrastructure and credit decisioning systems that power banking and spending management for companies globally. The company is hiring Backend Software Engineers II to develop scalable systems including underwriting engines, real-time data pipelines, and ML-powered credit models across 200+ markets.
- Website
- brex.com
Likely interview questions
- Walk us through a time you shipped an AI or automation system to production. What were the biggest technical and operational challenges, and how did you measure success?
- Tell us about your experience with LLM frameworks and agent architectures—specifically, how have you built systems using tool-use patterns like function calling or MCPs?