Sr. Software Engineer – Reference Data

Overview

As Sr. Software Engineer on Addepar’s Reference Data team, you will architect and deliver AI-native capabilities that power our platform. You’ll own end-to-end AI product delivery, from prototypes to scalable production services, focusing on reliability and performance. You’ll work with product teams to translate goals into robust AI solutions, shaping the wealth-technology landscape. This role combines hands-on execution with thought leadership in AI across the platform.

Responsibilities

• Drive end-to-end AI product delivery from prototype to production services
• Architect and productionize core AI platform components (LLMs, vector databases, prompt tuning)
• Develop agentic capabilities including MCP and cross-platform tool use, web browsing, and computer interactions
• Enforce technical discipline and operational excellence across AI products
• Iterate AI products using performance metrics and user feedback for continuous improvement
• Evaluate and adopt new AI technologies to stay at the frontier
• Collaborate with product managers and other engineering teams to translate strategic goals into technical solutions
• Define and implement platform architecture with growth and scalability in mind
• Provide technical leadership and prescriptive guidance on AI best practices

Key requirements

• B.S. or M.S. in Computer Science or similar field (or equivalent practical experience)
• Extensive software engineering experience
• Proven track record shipping complex, production-grade systems
• Experience shipping/maintaining AI-native products or demonstrable AI-related projects
• Strong ownership mindset and bias for action
• Experience with LLMs and agentic systems (preferred)
• Hands-on experience with modern AI/LLM tools (Databricks, Langchain, MLFlow – preferred)
• Understanding of probabilistic systems underpinning LLMs (preferred)
• Experience building AI products in finance or highly accuracy-sensitive domains (a plus)

• ownership mindset
• bias for action
• collaboration
• LLMs and agentic frameworks
• vector databases and prompt tuning
• MCP/cross-platform tool use

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