Role overview
A senior engineering role on a small, focused AI product team building practical, customer-facing experiences for a retirement savings product. The position centers on hands‑on implementation of large language model features, end-to-end ownership of problems, and rapid iteration from idea to production. It suits an engineer who enjoys making pragmatic technical calls and collaborating closely with product and design partners.
Responsibilities
- Design, build, and ship production features that use large language models to address real customer needs.
- Implement and integrate LLM components such as prompting strategies, tool‑use loops, retrieval‑augmented generation flows, evaluation harnesses, and monitoring.
- Make pragmatic architecture decisions, de‑risk tricky areas, and keep workstreams unblocked.
- Partner with product and design to refine concepts, shape scope, and iterate quickly on end‑to‑end experiences.
- Collaborate with adjacent engineering teams to integrate with existing systems and resolve dependencies.
- Write clear, reliable, well‑tested code that ships quickly and scales appropriately.
Requirements
- 7+ years of software engineering experience building and shipping production products or platforms.
- Hands‑on experience implementing LLM‑powered features, for example RAG flows, agents, tool loops, or workflow automation with language models.
- Strong engineering fundamentals across services, APIs, and data flows, with comfort spending most of the time coding.
- Experience running or heavily contributing to a team’s execution, including sprints, ticket tracking, scoping, and cross‑functional coordination.
- A bias to action: defaulting to trying things, iterating, and learning rather than waiting for direction.
- Proficiency with Ruby on Rails (or a comparable language) and React (or a comparable frontend library).
- Clear communication skills and the ability to work effectively with product, design, and other engineering teams without needing a formal management title.
Nice to have
- Familiarity with evaluation, monitoring, and observability practices for AI‑powered features.
- Experience operating within hybrid, multi‑office product organizations.
Benefits and work setup
- Targeted cash compensation of $187,000-$234,000 in Denver and most remote locations, and $227,000-$284,000 for San Francisco and New York, with final offers varying by experience and expertise.
- Stock equity in addition to base pay.
- Hybrid expectation of roughly 2-3 days per week in office for employees based in Denver, San Francisco, or New York; remote work permitted from other locations subject to a secure, reliable internet connection.
- Full‑time employees receive competitive base pay, benefits, and equity (RSUs).