Site Reliability Engineer

🚀 Senior Infrastructure Engineer – AI, Real\-Time Systems \& Scale \| Hybrid in San Francisco

Shape the future of AI\-powered voice technology.

Are you ready to build infrastructure that powers millions of real\-time AI phone conversations? We’re looking for a

Senior Infrastructure Engineer

to join a fast\-growing, venture\-backed team in San Francisco that’s redefining how enterprises communicate at scale.

This is not your average DevOps role. You’ll be architecting bleeding\-edge distributed systems, enabling ML inference at scale, and designing infrastructure that keeps real\-time conversations flowing—flawlessly.

What You'Ll Do:

Build for Scale:

Architect robust, low\-latency systems using Kubernetes and cloud infrastructure to support high\-volume voice processing.

ML Infra, but Real\-Time:

Design and optimize pipelines for training and inference of large AI models—with ultra\-low latency demands.

Wrangle Telephony:

Integrate with complex enterprise phone systems, SIP trunks, and VoIP layers. Bring new life to old\-school protocols.

See Around Corners:

Proactively anticipate scaling and reliability challenges as usage and customer expectations grow.

Battle\-Test Reliability:

Lead monitoring, alerting, and incident response efforts. 99\.999% uptime isn’t a target—it’s the standard.

Own Interesting Problems:

From streaming architectures to legacy system integration, you’ll invent and iterate on infrastructure no one’s built before.

You’ll Thrive Here If You:

Have

5\+ years building scalable, distributed systems

on cloud platforms like AWS or GCP.

Understand the deep internals—TLS, load balancing, failover strategies, and obscure RFCs are part of your daily vocabulary.

Have experience with

real\-time systems

(streaming, voice/video, high\-throughput environments).

Embrace the

startup mindset

: fast pace, ambiguity, and ownership don’t scare you—they energize you.

Bring strong opinions, lightly held—you can advocate for your ideas, collaborate, and find the best solution as a team.

Know your way around tools like

Terraform, Kubernetes, Docker, HAProxy, Go, TypeScript, Datadog, and GPU infra

.

Bonus Points If You Have:

Experience with

telephony protocols

(SIP, WebRTC, VOIP)

Background in

ML infrastructure or real\-time audio/video

Exposure to

NVIDIA hardware

and performance tuning for inference