Staff Software Engineer - Customer Facing Applied AI

Addepar is hiring a Staff Software Engineer for its Applied AI team, which works with strategic clients to identify and build AI capabilities for their workflows. The role builds agentic systems from discovery and prototyping through production, and partners with core engineering teams to turn client-specific solutions into products.

Responsibilities:

• Build AI-powered agents and tools end-to-end: identify the problem with clients and domain experts, prototype quickly, evaluate rigorously, and ship to production
• Design and implement LLM integrations including prompt engineering, function calling, structured outputs, and retrieval-augmented workflows
• Define evaluation criteria and build test harnesses for AI systems where correctness is ambiguous and domain-specific
• Work directly with clients to understand their workflows, validate AI behavior, and iterate toward production quality
• Partner with core engineering teams to generalize client-specific work into product features
• Contribute to shared AI infrastructure (agent frameworks, tool integrations, deployment pipelines)
• Monitor and maintain production AI systems with attention to reliability, cost, and performance

Requirements:

• 2+ years of professional software engineering experience
• Professional experience with Python (the team's primary language)
• Experience working with LLM APIs (Claude, GPT, or similar) including function calling and structured outputs
• Comfort with SQL for data exploration and building data-grounded AI workflows
• Experience with AI-assisted development tools (Claude Code, Codex, or similar)
• You've built something from zero to one
• You're drawn to figuring out what to build, not just building what's specified
• A bias for action
• You ship quickly, learn from feedback, and iterate
• You see projects through from prototype to production and care about what the client experiences, not what the code does
• BS/MS in Computer Science, Mathematics, or another quantitative field, or equivalent experience
• Experience building agentic systems (multi-step tool use, agent orchestration, durable workflows)
• Experience with additional backend languages (Java is valuable in our environment)
• Familiarity with financial data (portfolios, transactions, securities, accounting)
• Exposure to cloud infrastructure (AWS) and container-based deployments
• Experience with evaluation frameworks or methodologies for measuring AI system quality