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Senior Full Stack Engineer, AI Experience

San Francisco, CA, Washington, D.C., New York City, N.Y., Remote (USA)

ABOUT ROCKET MONEY đź”®

Rocket Money’s mission is to meaningfully improve the financial prosperity of millions of people. Rocket Money offers members a unique understanding of their finances and a suite of valuable services that save them time and money – ultimately giving them a leg up on their financial journey.

ABOUT THE TEAM 🤝

The EverPresence team is building the intelligence behind our next-generation financial assistant. We engineer robust backend systems (Node.js + TypeScript, PostgreSQL/pgvector, AWS) and push the envelope with cutting-edge Generative AI — integrating state-of-the-art models (OpenAI, Anthropic), retrieval-augmented generation (RAG) pipelines, and scalable LLM infrastructure. As a high-caliber, collaborative group, we value rapid experimentation, clean and reliable code, and earning user trust.
 
IN THIS ROLE, YOU'LL:
  • Spend approximately half of your time on LLM-driven work: crafting, refining, and evaluating prompts; designing and iterating RAG/data retrieval strategies; building prompt/response evaluation frameworks; and ensuring the quality and safety of model outputs.
  • Spend the other half building and scaling backend services in TypeScript/Node (GraphQL, PostgreSQL/pgvector, AWS) to serve, secure, and monitor our AI-powered user experiences.
  • Own features end-to-end: from technical design through implementation and infrastructure to monitoring.
  • Collaborate with product, design, and data teammates to transform user challenges into effective conversational solutions—rapidly iterating based on real-world feedback.
  • Stay on top of the latest GenAI research, open-source tooling, and best practices—advocating and implementing relevant innovations.
  • Contribute to team quality through code reviews, architecture discussions, and mentorship.

ABOUT YOU 🦄

  • 6+ years as a professional software engineer, with a strong focus on backend TypeScript/Node and SQL-based systems.
  • Demonstrated hands-on experience with large language models—spanning personal projects, prototypes, open-source, or production—including prompt engineering, model evaluations/benchmarks, and understanding of RAG workflows.
  • Solid SQL/data modeling skills, ideally with exposure to PostgreSQL (extra credit for pgvector).
  • Experience with or enthusiasm for LLM infrastructure tools such as LangFuse and LiteLLM, as well as cloud-based deployment (AWS preferred).
  • You’re passionate about GenAI and actively experiment with new models, evaluation tools, and AI workflows in your own time.
  • Self-driven, communicative, and motivated to deliver impactful solutions in a fast-moving, highly collaborative environment.

Bonus Points

  • Experience using LangFuse and/or LiteLLM for tracking prompts, responses, and evaluations
  • Familiarity with model observability stacks
  • Any exposure to fintech, security, or compliance challenges
If you’re a strong backend developer who’s deeply curious about LLMs, prompt engineering, and building impactful GenAI solutions, let’s connect!

WE OFFER đź’«

  • Health, Dental & Vision Plans
  • Life Insurance
  • Long/Short Term Disability
  • Competitive Pay
  • 401k Matching
  • Team Member Stock Purchasing Program (TMSPP)
  • Learning & Development Opportunities
  • Tuition Reimbursement
  • Unlimited PTO
  • Daily Lunch, Snacks & Coffee (in-office only)
  • Commuter benefits (in-office only)

Additional Information: Salary range of $150,000 - $185,000/year + bonus + benefits. Base pay offered may vary depending on job-related knowledge, skills, and experience.

Rocket Money is an Affirmative Action and Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, or protected veteran status and will not be discriminated against on the basis of disability.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

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