Senior Software Engineer (Reference Data)

• We are currently seeking a Sr. Software Engineer with an AI background to join our Reference Data team
• The reference data team at Addepar is responsible to manage the data reconciliation and curation process for market data providers to build a single source of truth and provide alignment across public and alternative investment data products for the client to manage their investment portfolio
• FAs an engineer on the team you will partner with product teams at Addepar to understand their business needs. The team is focused on enabling and building disruptive, innovative solutions to transform the wealth technology landscape with AI across our product suite and is growing quickly
• You will be responsible for architecting, leading, and driving the execution of the core AI capabilities that power our platform
• This is a high-impact role for an engineer who thrives on the frontier of applied AI and maintains a relentless focus on shipping
• You will not just work with LLMs and agentic frameworks; you will own the end-to-end delivery of AI-native products, ensuring that cutting-edge AI systems are hardened into robust, scalable, and high-performance production systems
• Drive the end-to-end execution and delivery of AI-native products, moving from initial prototype to scalable production services with high velocity
• Architect, build, and productionize core components of our AI platform – including:
• LLMs and the surrounding ecosystem (e.g., vector DBs, prompt tuning, etc.)
• Agentic frameworks
• Core agent capabilities like MCP/ cross-platform tool use, web browsing, computer use
• Enforce technical discipline and operational excellence for all of our AI products
• Rapidly iterate on AI products based on performance metrics and user feedback to drive continuous improvement
• Lead the evaluation and adoption of new AI technologies and frameworks to keep Addepar on the frontier of the industry
• Partner with product managers and other engineering teams to translate strategic goals into technical realities, abstracting AI complexity so they can iterate quickly
• Collaborate closely with our engineering leads to define and implement our platform architecture to meet Addepar’s growth and scalability needs
• Evaluate and drive strategic business and technology decisions for the company and for customers
• Be a technical thought leader in multiple areas who educates and shares best practices. Your contributions deliver strategic and prescriptive guidance

Benefits

• Equity: Stretch the idea of ownership beyond your day-to-day and take pride in being an owner in the growth of Addepar
• Global Hybrid Workforce: Whether you work remotely or on-site, you’ll have the opportunity to build and collaborate with colleagues around the world
• Flexible Time Off: Spend time traveling, celebrating with friends and family or relax on your schedule
• Benefits Packages: Competitive medical, dental and vision benefits along with a monthly wellness allowance to keep you healthy and happy
• Learning & Development Allowance: Your continued growth and development are important to us
• Dynamic Team: Strong investment in the best talent at the intersection of technology and finance- [Preferred] Experience building AI products in highly accuracy-sensitive domains such as finance is a plus
• Proven experience shipping and maintaining AI-native products or features, or demonstrable passion through significant personal projects in AI
• BS/MS in Computer Science, Statistics, Mathematics, or another quantitative field, or equivalent experience
• B.S., or M.S. in Computer Science or similar technical field of study (or equivalent practical experience)
• Proven track record of owning and shipping complex, production-grade systems
• Extensive software engineering experience
• [Preferred] Specific experience with LLMs and agentic systems
• Deep ownership mindset, seeing projects through to completion and raising the bar for execution across the team
• A strong bias for action, with a passion for shipping rapidly and iterating with feedback
• [Preferred] Advanced understanding of the probabilistic systems underpinning modern LLMs
• [Preferred] Hands-on experience with technologies in the modern AI/LLM ecosystem, such as Databricks, Langchain, or MLFlow