Rocket Money - Staff Applied AI Engineer

• Build, evolve, and operate the Capabilities Engine as a core platform for agentic product behavior. • Design abstractions that make capabilities easy for agents and methods to discover, invoke, compose, observe, and improve. • Partner with method owners across all contact method subagents as well as supporting agents to make each method more agentic and better integrated with the Capabilities Engine. • Identify repeated patterns across methods and turn them into reusable capabilities, interfaces, evaluations, and operating practices. • Provide guidance on how to monitor and optimize reliability, latency, cost, observability, and safety of capability execution in production. • Develop practical evaluation loops for agentic behavior, including offline tests, production metrics, traces, regression suites, and qualitative review. • Debug complex failures across model behavior, tool execution, orchestration, product constraints, and downstream systems. • Translate ambiguous product needs into shippable technical plans, prototypes, and durable systems. • Create the primitives capability teams need to successfully produce shippable technical plans, prototypes, and durable systems. • Collaborate with product, design, data, platform, and operations partners to ensure capabilities are useful, understandable, and safe for real users. • Mentor and develop engineers across the Agents Group, acting as a technical multiplier by sharing expertise, fostering a culture where others feel empowered to propose ideas and take on high-impact work, and helping shape the group's overall technical direction and standards. • Document system behavior, capability contracts, tradeoffs, and operational expectations so other teams can build on the platform confidently. • Drop into high-priority method work when needed to accelerate agentic behavior, unblock launches, or stabilize production issues. • Stay curious about the frontier of applied AI engineering and help the team adopt new tools, patterns, and workflows where they make the product more dependable. • YOU MAY BE A GOOD FIT IF YOU: • Have experience shipping production software with strong engineering fundamentals. • Are excited by AI as an execution layer. You understand that durable customer value comes from orchestration, tools, workflows, evaluation, approvals, observability, and recovery. • Have hands-on experience or strong practical familiarity with LLM applications, agentic systems, tool calling, retrieval, workflow orchestration, or applied AI products. • Have strong product judgment. You can reason from customer need to product experience to technical architecture, and you know the difference between a clever demo and a dependable customer-facing system. • Are comfortable working across ambiguity. You can take a broad problem, identify the crux, sequence the work, make tradeoffs, and keep moving without waiting for perfect clarity. • Like building platform abstractions and shared services that help multiple teams or product surfaces move faster. • Hold a high bar for quality, reliability, and trust. You know that systems acting on behalf of users need clear contracts, observability, evaluation, and recovery paths. • Are energized by debugging messy production behavior across model outputs, distributed systems, product logic, user context, and downstream integrations. • Communicate clearly with engineering, product, design, data, operations, and platform partners. • Are motivated to actively mentor and grow others. • Are a tenacious learner who stays engaged with the frontier of AI engineering without chasing every shiny object. • Are motivated by measurable customer and operational impact, not just interesting technical demos. • STRONG CANDIDATES MAY HAVE: • Experience building production LLM applications, agentic systems, workflow engines, tool-calling systems, or AI-powered product surfaces. • Experience designing capability platforms, shared services, orchestration systems, or reusable product infrastructure. • Experience with TypeScript, Node, GraphQL, Postgres, LLM applications, agentic systems, and workflow orchestration. • Experience creating evaluation and observability systems for probabilistic or agentic behavior. • Experience improving reliability, alerting, tracing, incident response, operational quality, or other systems that make software safer and more dependable over time. • Experience partnering deeply with Product, Design, Data, Operations, Legal, Support, or other cross-functional teams in a sensitive customer domain. • Firsthand experience building products where customer trust, permissions, explainability, and recovery paths are central to the experience. • A visible habit of turning repeated one-off work into reusable systems, patterns, and documentation.