Software Engineer - Customer Facing Applied AI

Addepar

17h•$163,000 - $203,500

About The Position

The Applied AI team works with Addepar's strategic clients to identify opportunities for AI, build capabilities from scratch, validate them, and partner with core engineering teams to ship them as products for all customers. The engineer builds agentic systems from concept through production, including understanding problems, prototyping, designing agent behavior, integrating tools, and developing evaluations. The role works closely with end users, using their feedback to improve the product and shape the work of this new team. The position 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. Applicants must be legally authorized to work in the United States without current or future visa sponsorship and must be authorized to begin work in the U.S. on their first day.

Requirements

At least 2 years of professional software engineering experience.
Professional experience with Python.
Experience working with LLM APIs such as Claude or GPT, including function calling and structured outputs.
Comfort with SQL for data exploration and building data-grounded AI workflows.
Experience with AI-assisted development tools such as Claude Code or Codex.
Experience building something from zero to one, with an interest in determining what to build as well as building it.
Bias for action, ability to ship quickly, learn from feedback, and iterate.
Ability to take projects from prototype to production and care about the client experience.
BS/MS in Computer Science, Mathematics, or another quantitative field, or equivalent experience.

Nice To Haves

Experience building agentic systems, including multi-step tool use, agent orchestration, or durable workflows.
Experience with additional backend languages; Java is valuable in Addepar's environment.
Familiarity with financial data such as portfolios, transactions, securities, or accounting.
Exposure to cloud infrastructure such as AWS and container-based deployments.
Experience with evaluation frameworks or methodologies for measuring AI system quality.

Responsibilities

Identify problems with clients and domain experts, then build AI-powered agents and tools from prototype through evaluation and 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 with ambiguous, domain-specific correctness.
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, including agent frameworks, tool integrations, and deployment pipelines.
Monitor and maintain production AI systems, with attention to reliability, cost, and performance.