Vapi
Technology
MemberofTechnicalStaff,CoreBackend
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Member of Technical Staff, Core Backend at Vapi. Skills: Streaming systems, Backpressure, Provider abstractions. Own pipeline stability. Own pipeline pluggability”
What You'll Achieve.
Drive measurable reliability win; Drive measurable latency win
Industry & Context.
Debugged incidents; Root cause analysis
What They're Looking For.
Must Have
Built real-time or streaming systems, Debugged a backpressure cascade, Operated Postgres at scale, Instrument with OpenTelemetry
Nice to Have
TypeScript + Node.js + NestJS
What You'll Do.
Own pipeline stability
Own pipeline pluggability
Consolidate BullMQ into Kafka
Harden provider abstractions
Instrument pipeline with tracing
Shore up Postgres SPOFs
Land pipeline improvement
Ship event-driven instrumentation
Harden provider plugin path
Drive reliability win
Pull in for design reviews
How You'll Work.
Team & Collaboration
Agents teams; FDE teams
Full Job Description
Voice AI that resolves, not transfers. Most phone systems trap callers in menus and scripts. Vapi is the platform for deploying voice agents that know your business and can listen, adapt, and resolve in minutes. - The numbers: 1 billion calls. 1 million developers. 10x enterprise ARR growth - The customers: Amazon Ring, ServiceTitan, New York Life, Intuit, Kavak, and thousands more, from YC startups to the Fortune 500 - The news: a $50M Series B led by Peak XV Partners, with Bessemer Venture Partners, Kleiner Perkins, M12 (Microsoft's Venture Fund), Y Combinator, and our earlier backers. Total raised: $72M Why We’re Hiring This Role: The StreamModule pipeline — VAD → STT → LLM → TTS → Transport — runs on cork/uncork backpressure during live phone calls. A hundred milliseconds of delay is audible. This role owns pipeline stability and pluggability, so the agents and FDE teams can add new models and providers without touching core. You’ll consolidate BullMQ into Kafka, harden the provider abstractions (LLM, STT, TTS base classes), instrument the pipeline with event-driven OTEL tracing, and shore up the Postgres SPOFs that contributed to the Oct 15 and Oct 22 incidents. What You’ll Do: 30 Day: Ramp on the StreamModule pipeline and the cork/uncork backpressure model. Walk the Oct 15 / Oct 22 DB incidents and the duplicate-message incident. Land a scoped pipeline or provider-abstraction improvement. 60 Day: Own a slice of the BullMQ → Kafka consolidation. Ship event-driven OTEL instrumentation for at least one critical pipeline stage. Harden one provider plugin path so a new model can be added without core changes. 90 Day: Drive a measurable reliability or latency win on the call path. Be the backend owner that agents and FDE teams pull in for design reviews on new providers and pipeline changes. Who You Are: Must-haves: - You’ve built real-time or streaming systems in production — media pipelines, streaming data, or event-driven backends. You’ve debugged
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