Senior Software Engineer, Wealth.com

• Degree in Computer Science, Engineering, or equivalent practical experience
• Strong software engineering fundamentals (system design, data structures, clean code)
• Proven track record of building and shipping production systems
• Proficiency in Python and/or TypeScript. Must be comfortable working across a polyglot stack, picking up new languages and frameworks as needed
• Experience building AI-powered applications in production
• Hands-on experience with agentic or multi-step AI systems
• Experience working with APIs or tool integrations within AI systems
• Ability to operate in ambiguous, fast-moving environments

Desirable

• We are seeking a Software Engineer, AI to build the next generation of AI-powered applications. This is a builder role. You will design and ship production-grade, agentic systems that deliver real user value — not prototypes or demos
• You will operate at the intersection of AI, product, and engineering, taking ambiguous ideas and turning them into scalable, user-facing experiences. From document understanding to multi-step workflows, your work will directly shape how users interact with AI
• Own problems end-to-end:
• Take loosely defined ideas and turn them into shipped, production features
• Make architectural decisions across AI systems, backend, and frontend
• Build real AI products (not demos):
• Design, build, and deploy production-grade systems leveraging LLMs
• Own systems that handle real user traffic, constraints, and failure modes
• Develop agentic systems:
• Build multi-step workflows with reasoning, tool use, and structured outputs
• Design systems that balance autonomy, control, and reliability
• Design AI-native user experiences:
• Build AI-native interfaces (agentic UIs) where users collaborate with AI
• Implement streaming, feedback loops, and iterative workflows
• Work with tools / skills (AI capabilities):
• Integrate and compose tools (“skills”) such as APIs, retrieval systems, and external actions
• Design product features that leverage these capabilities effectively within agent workflows
• Collaborate with AI Infrastructure to improve tool interfaces and behavior
• Drive quality and performance:
• Build evaluation and feedback loops
• Implement guardrails and monitoring
• Optimize for latency, cost, and accuracy
• Work across the stack:
• Backend services, APIs, data pipelines, and frontend experiences
• Ship quickly and iterate based on real usage