Engineering Manager

About the role

We live in a world where technology is rapidly changing the educational experiences of students and teachers everywhere, and we have the opportunity to shape how this change takes place. Kira's mission is to harness transformative AI technologies to make world-class personalized teaching and learning accessible to everyone. We are a rapidly growing startup backed by top-tier Venture Capital funds including New Enterprise Associates (NEA), Andrew Ng's AI Fund, and Primavera.

As an Engineering Manager, you will lead a team responsible for significant product and platform capabilities. You are accountable for the people on your team, the technical quality and reliability of what they build, and whether the work reaches useful outcomes for real classrooms.

This is a deeply technical management role. We expect Engineering Managers to have the judgment of a strong senior or staff-level engineer: able to challenge system designs, review consequential code, debug production issues, reason about performance and failure modes, and coach engineers from concrete experience. You should still write code when it is the highest-leverage thing to do, but your success is measured by the capability and output of your team, not your personal commits.

You will work closely with Product, Design, QA, and engineering leaders to turn ambiguous goals into clear problems, pragmatic plans, and reliable software. Kira’s environment includes a large monorepo spanning frontend, backend, AI services, and real-time collaboration; multiple platform generations; complex education workflows; and teams distributed across the US and APAC. Ramping takes time, and we plan accordingly. This role requires flexibility with work hours and occasional availability outside regular business hours, and is based in San Francisco or New York City, working from the office four days per week (Wednesdays WFH).

What You'll Do

- Turn company priorities into clear problems, ownership, and honest milestones; find the critical path, surface risk early, test assumptions, and cut scope intelligently.

- Stay close enough to the architecture, codebase, and production systems to evaluate technical decisions independently; review consequential work and coach engineers on system design, data modeling, debugging, performance, reliability, code quality, and security.

- Give people meaningful ownership, coach them through increasing ambiguity, and develop senior engineers and technical leads by delegating consequential decisions.

- Set expectations early, give feedback in the moment, recognize strong performance, and address gaps before review time.

- Ensure the team designs for realistic failure modes and peak load, operates what it ships, responds effectively to incidents, and turns failures into durable improvements.

- Partner closely with Product and Design — help define the problem instead of accepting requirements, and make sure what ships works in real classrooms, not just in demos.