It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of joy. Backed by Greylock and Sequoia.
About the role
As a Principal Engineer in HRSD, you set the technical direction for model-driven capability across the HR domain, the agentic and conversational experiences that interpret employee, manager, and HR agent intent, reason over profile, case, catalog, policy, and knowledge context, and act on the user's behalf. Your unit of ownership is the architecture, standards, and evaluation infrastructure that let multiple teams ship those experiences safely — not a single feature set.
What they're looking for
- 10+ years of software engineering, with a record of technical direction adopted beyond your immediate team
- Direct, hands-on experience authoring agentic instructions and prompts, designing autonomous workflows, and building the evaluation that verifies them
- Production experience with LLM APIs, retrieval-grounded features, agent orchestration, tool and function calling, and structured output enforcement — including at least one model or orchestration migration carried through production
- Entitlement-aware data handling: per-user access enforcement in retrieval and tool layers, scope separation between roles, and handling of restricted records under audit
- Evaluation experience used by engineers other than yourself: dataset curation, judge calibration, CI gating, drift detection
- Strong command of system design, APIs, data modeling, and testing across front-end, server-side, and relational data work with a high quality mindset
More about this role
The Role
As a Principal Engineer in HRSD, you set the technical direction for model-driven capability across the HR domain, the agentic and conversational experiences that interpret employee, manager, and HR agent intent, reason over profile, case, catalog, policy, and knowledge context, and act on the user's behalf. Your unit of ownership is the architecture, standards, and evaluation infrastructure that let multiple teams ship those experiences safely — not a single feature set.
This is not an ML research role; you do not train foundation models. It is also distinct from traditional full-stack staff work, where systems follow deterministic logic. Two things shape the job: much of the load-bearing logic lives in natural language — instructions, prompts, context, tool descriptions, guardrails — and must be engineered with the same discipline as code; and because behavior is probabilistic, correctness is established through evaluation at scale, not fixed assertions.
AI architecture for the domain. How agents are decomposed and composed, where reasoning happens, how context is assembled and bounded, how tools are exposed, and how autonomy is delegated. Own the multi-release calls:...
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