Addepar - Software Engineer - Customer Facing Applied AI
Requirements
• Prior 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 • Our Values • Act Like an Owner - Think and operate with intention, purpose and care. Own outcomes. • Act Like an Owner - • Build Together - Collaborate to unlock the best solutions. Deliver lasting value. • Build Together - • Champion Our Clients - Exceed client expectations. Our clients’ success is our success. • Champion Our Clients - • Drive Innovation - Be bold and unconstrained in problem solving. Transform the industry. • Drive Innovation - • Embrace Learning - Engage our community to broaden our perspective. Bring a growth mindset. • Embrace Learning -
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
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