Sierra, a conversational-agent startup co-founded by Bret Taylor, has redesigned its engineering interview process to reflect how the job has changed in the age of coding agents (Codex, Claude Code). The authors — Vijay Iyengar, Arya Asemanfar, and Angie Wang — argue that the engineer's role is shifting from "building the machine" to "designing and refining the machine," by analogy with how engineers stopped worrying about the compiler's translation from code to machine instructions. Now that a single engineer can build across the entire stack, value comes from combining technical capability, product thinking, and business context.
The starting observation: the legacy process (two coding interviews, algorithms, system design, culture fit) mostly captured mechanics — typing syntax, recalling algorithmic details, assembling frameworks. That signal grew increasingly dissonant with the day-to-day reality of the work. Hiring managers compensated by falling back on referrals and prior experience.
Three criteria guided the redesign: representativeness (reflects real work), high signal (clarity on where a candidate excels or needs support), and a positive experience. The centerpiece is a three-part "AI-native onsite." Plan: a working session where the candidate ideates a product in their domain, with interviewers asking questions to sharpen the idea. Build: 2 hours solo, using AI tools and frameworks of the candidate's choosing, with full freedom to pivot. Review: a demo, a discussion of product choices, a code review (data model, abstractions, extensibility), and a conversation about the path to production and how AI was used. Candidates are allowed to cut scope and skip boilerplate, per Paul Buchheit's formula: "if it's great, it doesn't have to be good."
The rest of the process followed suit. The coding phone screen (no AI, in an online editor) is replaced by a system design interview — since vibe coding is easy, the real challenge is scalable production deployment. A "debugging interview" is being piloted to capture 1→N work in existing codebases: the candidate reviews a cross-cutting PR with agents.
Lessons learned: hiring is for strengths, not the absence of weaknesses; debriefs have shifted from "should we hire this person?" to "where will this person excel?". Candidates report more engaging interviews — one built an AI flow game, a backend engineer drove their demo through an agent and a markdown file. Challenges (standardization, calibration) are mitigated by criteria that are agnostic to the product built and by paired interviewers. The format also applies to infra, where engineers now build full-stack and integrate vertically with the product.