Staff Applied AI Engineer

Rocket Money•Washington, DC

17h•$200,000 - $270,000•Onsite

About The Position

As an Applied AI Engineer, you will help build, evolve, and operate the Capabilities Engine: the core platform that makes agentic behavior reliable, reusable, observable, and production-ready across Rocket Money’s AI-powered experiences. You will create the primitives that capability teams need to turn ambiguous product needs into shippable technical plans, prototypes, and durable systems. You will also partner with contact-method owners across phone, email, chat, web login, ODC, and autonomous workflows to make each method more agentic and better integrated with the Capabilities Engine. That means helping agents plan, discover and invoke capabilities, recover from ambiguity, handle real-world edge cases, and complete user-facing work safely and correctly. This role is for an engineer who is excited by—and experienced in—AI as an execution layer, not just a conversation layer. You will build systems that let agents use tools, observe outcomes, recover from failure, and earn enough trust to act on behalf of users. You will work across product and platform boundaries, moving between architecture, implementation, debugging, evaluation, and cross-functional collaboration. Success means making the Capabilities Engine easier to extend, operate, and integrate with; improving task completion and recovery across methods; and ensuring new capabilities ship with clear contracts, tests, traces, ownership, and production-readiness expectations.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.