Backend Engineer (Remote)

About Kinterview: Kinterview (kinterview.cc) is a real-time AI interview copilot. It listens to interviewers' questions and streams tailored answers to a private window, in 23 languages, on Zoom, Google Meet, and Microsoft Teams. We're a remote-first team on a mission to end interview anxiety for job seekers everywhere.

Location: Remote, work from anywhere (some overlap with UTC+8 mornings preferred).

What we offer: Competitive salary; fully remote and async-first; annual team offsite; learning & equipment budget; early-stage equity.

How to apply: Email your resume and a short note to [email protected] with the role title in the subject line.

01What you'll do

• Design and scale the APIs and real-time pipelines behind Kinterview's live answer streaming.
• Own reliability for a product people depend on in high-stakes moments.
• Build integrations with Zoom, Google Meet, and Microsoft Teams.
• Keep our infrastructure costs sane as usage grows.

02What you bring

• 3+ years building production backends (Node.js, Python, or Go).
• Experience with streaming architectures (WebSockets, SSE) and queues.
• Solid grasp of auth, rate limiting, and multi-tenant data isolation.
• You write the runbook before the incident, not after.

03Nice to have

• Experience with speech-to-text or LLM inference pipelines.
• Startup experience shipping under tight deadlines.

04About the product you'll be working on

Kinterview is a real-time AI interview copilot. When a candidate is in a live interview on Zoom, Google Meet, or Microsoft Teams, Kinterview listens to the interviewer's questions and streams tailored answers to a private window, in 23 languages.

Interviews are high-stakes moments, and the product exists to support candidates in that moment: the transcription has to keep up, the answers have to fit the question being asked and the candidate's own background, and the interface has to stay calm and out of the way while the candidate is under pressure. Every part of the product serves that moment.

05How the team works

We are a remote-first company and a small team (1-10 people), hiring remotely worldwide. We work async-first and in writing across time zones, with some overlap with UTC+8 mornings preferred so we can meet live when it matters. There is no handoff theater: the people who design and build a feature are the people who hear from users about it, and everyone owns their work end to end, from idea to shipped, measured, and iterated. If you like small teams where your work reaches users directly, you will fit in here.

06What we offer

Competitive salary, fully remote and async-first work, an annual team offsite, a learning and equipment budget, and early-stage equity.

07How to apply

Email your resume and a short note to [email protected] with the role title in the subject line. Tell us briefly why this role and what you have built or done that is most relevant.

More on this role: your work is the backbone of the live answer itself. You will design and scale the APIs and real-time pipelines behind Kinterview's live answer streaming, so that a question heard in a meeting becomes a tailored answer in the candidate's private window without delay and without breaking under load.

Reliability is not an abstract goal here: you will own it for a product people depend on in high-stakes moments, which means thinking ahead about failure modes, building the integrations with Zoom, Google Meet, and Microsoft Teams that the product depends on, and keeping our infrastructure costs sane as usage grows, scaling what matters and not paying for what does not.

That work draws on three or more years building production backends in Node.js, Python, or Go, on streaming architectures using WebSockets and server-sent events and queues, and on a solid grasp of auth, rate limiting, and multi-tenant data isolation, so one customer's data and traffic stay separate from another's. You write the runbook before the incident, not after, so that when something does go wrong the team knows exactly what to do.

Experience with speech-to-text or LLM inference pipelines will help where the backend meets the parts of the product that transcribe and reason, and startup experience shipping under tight deadlines will help because this is a small team where the backend you ship reaches users directly.