About us
Infer is building the operating system for insurance agencies. We make AI agents(including voice agents) that handle the work agencies have always done by hand: qualifying inbound leads, helping producers during live calls, auditing calls after, running renewals, and bringing churned customers back.
Our long bet is that AI eventually sells insurance directly. Agencies are the wedge because that is where the work, the data, and the customer relationships actually live. Get good there, and the rest follows.
We are a YC company and have raised from Stellaris Venture partners and others. Founders are: Vaibhav, Urvin and Suneel. Vaibhav was an architect and AI researcher(at Purdue) now a licensed insurance agent. Urvin worked at BCG, is a surfer with six pack abs. Suneel is an IITian and a philomath.
A few reasons to join us:
We like pushing each other on team to test the limits because that's when you rediscover yourself.
We’re paranoid about making customers succeed (we challenge whats already good)
We love people who question-challenge-build.
We're highly transparent founders to work with & love getting challenged.
Finally, we love people who’re interdisciplinary.
About the role
You'll be a core builder on the team that ships our voice-AI platform owning the slice of the stack where real-time performance, test infrastructure, and production reliability meet. You'll drive down voice bot latency, keep our voice pipeline healthy across version bumps, and build the observability and test loops that let us ship fast (and increasingly let agents ship for us).
What you'll do
Optimize end-to-end voice bot latency and turn-taking behavior across STT → LLM → TTS pipelines
Scale backend infrastructure to handle concurrent voice sessions reliably
Build test infrastructure for voice agents that makes correctness easy to verify on every PR paving the way for agent-generated patches
Design and maintain routing-setup tests for our voice flows
Run monitoring and alerting; triage production issues and close the loop back into tests
Enable agent-driven patching of minor production issues, with humans staying in the loop on the right things
Must-haves
Experience building production backend systems, ideally in real-time, streaming, or low-latency contexts
Hands-on experience with async/concurrent systems in python asyncio, event loops, streaming pipelines
Deep familiarity with one or more voice/AI building blocks: STT, TTS, LLM streaming, SIP/WebRTC or telephony
Proven track record optimizing latency in a production system (profiling, tracing, p95/p99 work)
Solid database fundamentals: schema design, indexing, query optimization, and migration discipline
Solid test engineering instincts: unit, integration, and end-to-end testing for systems with non-deterministic components
Worked with distributed tracing tools (OpenTelemetry, Sentry, Datadog APM)
Production observability experience instrumenting services, designing useful alerts, running incident response
Comfortable with infra work Docker, AWS, CI/CD
Comfort owning a system end-to-end: design, ship, monitor, debug, iterate
Early-stage startup experience and the instinct to ship pragmatically
Contributed to open-source voice or real-time AI infrastructure
Direct experience with voice agents orchestration frameworks
Nice-to-haves
Built or maintained eval/test harnesses for LLM or agent-based systems
Experience with agent-driven workflows in production codebases
Background in turn-taking, VAD, or endpointing optimization for conversational AI