Software Engineer, Addepar

• 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

Desirable

• The Applied AI team works embedded with Addepar's strategic clients to figure out where AI can change how they work and then build those capabilities from scratch
• You'll spend time understanding a client's world, identify what's worth building, prototype it, prove it works, and then partner with core engineering teams to ship it as product for all customers
• As an Applied AI Engineer, you'll build agentic systems from idea through production
• The work starts with understanding the problem well enough to know what to build, then moves fast through prototyping and iteration
• You'll design agent behavior, wire up tool integrations, tune evaluations until the AI actually works in practice
• You'll sit close to the people who use what you build, and that feedback loop is how both the product and your skills grow fast
• The team is new
• Your work shapes what we ship
• This role may require occasional work outside standard business hours to accommodate client time zones and up to 15% travel to client sites, including occasional overnight and multi-day engagements
• 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