Auto-apply is a Pro feature

Let Findyet apply to this job for you:

  • Your CV is tailored to this posting.
  • The application form's questions are answered from your profile.
  • You review everything and approve before anything is sent.
  • Findyet submits the application and adds it to your tracker.

Available on Greenhouse postings today, up to 3 applications a day on Pro.

The first 100 verified accounts get Pro free for 90 days.

See plans

Addepar

NewBe an early applicant

Software Engineer (Customer Facing Applied Artificial Intelligence)

Posted
Discussion

Summary

Addepar's Applied AI Engineer works embedded with strategic clients to identify AI opportunities, prototype agentic systems using Python and LLM APIs, and partner with core engineering to ship production AI features, with occasional client travel and off-hours collaboration.

  • 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

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
  • A bias for action. You ship quickly, learn from feedback, and iterate
  • Professional experience with Python (the team’s primary language)
  • Comfort with SQL for data exploration and building data-grounded AI workflows
  • Experience with AI-assisted development tools (Claude Code, Codex, or similar)
  • 2+ years of professional software engineering experience
  • You’ve built something from zero to one. You’re drawn to figuring out what to build, not just building what’s specified
  • BS/MS in Computer Science, Mathematics, or another quantitative field, or equivalent experience
  • You see projects through from prototype to production and care about what the client experiences, not what the code does
  • Experience working with LLM APIs (Claude, GPT, or similar) including function calling and structured outputs
  • 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)
  • Experience with evaluation frameworks or methodologies for measuring AI system quality
  • Exposure to cloud infrastructure (AWS) and container-based deployments

Skills

Apply

See also

Software Engineering jobs by country — openings, pay and top skills →

Tailor your CV for this role?

We couldn't check your fit for this role — add a CV to your profile to see it next time.