Software Engineer, Brex

• We currently require a minimum of three coordinated days in the office per week, Monday, Wednesday, and Thursday
• 4+ years in engineering, technical product/program management, applied AI, or a similar technical role, with a track record of shipping AI/automation systems to production
• Hands-on experience building with LLMs, agent frameworks, and tool-use patterns (e.g., MCP, function calling, RAG) across the full stack (data, APIs, orchestration, product)
• Experience designing and optimizing SQL and/or NoSQL databases, including data modeling, query performance tuning, and schema design
• Strong ability to decompose human workflows into scalable, agentic systems, grounded in a deep curiosity for how people work
• High ownership and cross-functional influence, with the ability to quickly build trust across teams and seniority levels

Desirable

• Engineering at Brex is about building systems that scale with speed and intention
• Our teams span Software, Data, Security, and IT, and operate with high autonomy and deep collaboration
• We tackle hard technical problems, own our outcomes, and push for excellence at every level — from architecture to deployment
• It’s an environment where engineering is a craft, and builders become leaders
• We're building AI agents to automate and augment internal functions at Brex, and we're looking for a hands-on builder to help us do it
• You'll embed with teams across the company to understand how they actually work, then design, build, and ship agents that deliver real outcomes
• You'll work on top of existing models and frameworks, wiring together tools, MCPs, and internal systems into agentic workflows
• This is a technical role where you'll spend most of your time building, shipping, and iterating alongside the people whose work you're automating
• Embed with partner teams to deeply understand workflows, including shadowing and learning different job functions
• Scope, design, and deploy agents that take over real workflows across internal orgs
• Integrate agents with internal systems, APIs, and data sources
• Define evaluation frameworks, success metrics, and feedback loops to measure and improve agent performance
• Build shared tooling and contribute to playbooks that accelerate deployments, while owning reliability, adoption, and business impact