Senior Infrastructure Engineer — Real-Time AI Telephony

Bland; San Francisco (CA); On-site; USD 120,000 - 200,000; Full time

Benefits offered by this job

Healthcare benefits

Equity

Tools provided

SF office with rooftop

Job summary

Bland in San Francisco is seeking a Senior Infrastructure Engineer to design and operate scalable, distributed systems that power real-time voice processing for AI phone agents. You will own the backbone, optimize latency, and ensure 24/7 reliability across regions, integrating with enterprise telephony.

You’ll work with Kubernetes, cloud platforms (AWS/GCP), and ML infra to support training and inference, while advancing security, monitoring, and incident response.

Qualifications

• 5+ years building and scaling distributed systems, with deep knowledge of cloud infrastructure (AWS/GCP preferred).
• Experience with real-time processing, low latency workloads, streaming, or similar.

Responsibilities

• Contribute to scalable architecture design and build distributed systems with Kubernetes for high-volume, real-time voice processing.
• Build and support ML infrastructure from training pipelines to real-time inference serving across regions.
• Integrate with enterprise telephony, SIP trunks, and VoIP infrastructure.
• Maintain reliability with monitoring, alerting, and incident response for 24/7 uptime.
• Scale with growth by anticipating and solving capacity challenges.
• Ensure security and compliance for enterprise customers in regulated industries.

Skills

Distributed systems

Real-time processing

Cloud infrastructure (AWS/GCP)

Startup mentality

TLS/load balancing fundamentals

Tools

Cloudflare

HAProxy

Go

TypeScript

Datadog

Terraform

Docker

Kubernetes

Nvidia NVLink

Job description

Bland in San Francisco is seeking a Senior Infrastructure Engineer to design and operate scalable, distributed systems that power real-time voice processing for AI phone agents. You will own the backbone, optimize latency, and ensure 24/7 reliability across regions, integrating with enterprise telephony.

You’ll work with Kubernetes, cloud platforms (AWS/GCP), and ML infra to support training and inference, while advancing security, monitoring, and incident response.