Complete QA platform for voice agents. Backed by Y Combinator and AI Grant.
About the role
Own core services in TypeScript/Node.js and Python that orchestrate LiveKit , Temporal , STT/TTS, and LLM tooling for real-time voice agents. Scale 1 → N → 100× : take what works today and harden it for 10K parallel calls with 99.99% uptime. Turn human playbooks into productized systems.
What they're looking for
- Have senior/staff experience running distributed backends with real-time/streaming constraints
- Are fluent in TypeScript/Node.js and comfortable jumping into Python for ML/audio jobs
- Know Temporal (or similar workflow engines), queues, Redis, and PostgreSQL
- Have shipped production LLM apps and understand prompt/tool design, evals, and guardrail instrumentation
- Operate cloud-native on AWS with Terraform , k8s doesn’t scare you
- Are a power user of Cursor/Zed/Devin and were using code-gen before it was cool
More about this role
Hamming automates QA for voice AI agents. Everyone is building voice agents. We secure them. In fact, we invented this category. With one click, thousands of our agents call our customers’ agents across accents, background noise, and personalities—then we generate crisp bug reports and production-grade analytics. Reliability is the moat in voice AI, and that’s our whole job.
We are one of the fastest engineering teams in the world. We prod deploy 4x / day.
I’m looking for someone who can own reliability and scale across our LLM-enabled platform, shipping precise, outcome-driven improvements to high-availability systems.
— Sumanyu (CEO)
Previously: grew Citizen 4× and scaled an AI sales program to $100Ms/yr at Tesla.
Devin Case Study
Ranked #1 Eng team
OpenAI Dev Day 100billion token list
Own core services in TypeScript/Node.js and Python that orchestrate LiveKit , Temporal , STT/TTS, and LLM tooling for real-time voice agents.
Scale 1 → N → 100× : take what works today and harden it for 10K parallel calls with 99.99% uptime. Turn human playbooks into productized systems.
Harden pipelines for ingestion, evaluation, and analytics so telephony events, recordings, and outcomes...
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