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San Francisco, California, United States
Onsite
Simple AI is building state of the art voice AI agents for enterprise. Our realistic agents help iconic businesses like Doordash, xAI, and Omaha Steaks handle all kinds of phone operations, from customer support to order intake to lead qualifications. We are growing revenue like crazy and looking to hire a couple founding software engineers to help us stay afloat of current customer demand and also build out systems for the future. We are very excited about the potential for AI to change the world, and work out of our SF based office.
Simple AI raised $14M from world-class investors and operators like Y Combinator, First Harmonic, Massive Tech Ventures, Michael Seibel, Jared Friedman, Alexandr Wang, Matt Van Horn, Suhail Doshi, and Scott Wu.
The founders (Catheryn and Zach) met while running software teams at YC, where they shipped tons of new products for YC founders, including YC’s co-founder matching site. Cat previously worked at Instagram and Facebook and earned degrees in math and computer science at MIT. Zach previously studied computer science and statistics at U Chicago and led software at PEAK6. We have grown into a strong team of 20+ and are continuing to scale quickly.
Simple AI builds AI voice agents that handle real customer conversations over the phone. Our agents help businesses generate revenue and improve customer retention.
We’re hiring a Platform Engineer to build and run the infrastructure behind those conversations. Real-time voice has demanding reliability requirements: latency is immediately noticeable, calls are stateful, and deployments need to happen without interrupting customers.
You’ll work directly with the founders and engineering team to make our platform reliable, scalable, and easy to build on. This is a hands-on engineering role with meaningful ownership over infrastructure architecture and how we ship software.
Competitive salary & meaningful equity
Employee-friendly equity terms
An office located in SF
Health, dental & vision insurance
Free lunch & dinner at the office
Late night Ubers
Regular team events & offsites
Build and operate AWS infrastructure using Terraform and Kubernetes, including EKS clusters, networking, IAM, load balancing, and secrets management.
Develop reusable infrastructure modules and consistent development, staging, and production environments.
Build deployment workflows with automated checks, clear deployment visibility, and reliable rollbacks.
Design autoscaling and graceful shutdown behavior for services handling long-running voice conversations.
Improve observability across infrastructure and application services, with useful dashboards, actionable alerts, and clear reliability and latency metrics.
Investigate production issues, improve incident response, and address recurring problems at their source.
Partner with application and AI engineers to deploy new services, improve performance, and manage infrastructure costs.
Make it easier for engineers to provision resources, deploy changes, and debug their services safely.
Hands-on experience running Kubernetes and AWS infrastructure in production.
Strong Terraform experience, including maintainable modules, state management, and safe infrastructure changes.
A solid understanding of Linux, containers, networking, and distributed systems.
Experience building CI/CD pipelines and operating services through deployments, incidents, and growth.
Ability to write software and automation in a language such as Python, Go, or TypeScript.
Good judgment about when to introduce new infrastructure and when to keep things simple.
Someone who takes ownership, communicates clearly, and follows problems through to a lasting fix.
Experience with Argo CD, Helm, or other GitOps tooling.
Familiarity with real-time audio, SIP, WebRTC, or other latency-sensitive systems.
Experience operating PostgreSQL, Redis, queues, and background workers.
Experience deploying GPU workloads or self-hosted AI inference.
Familiarity with infrastructure security and supporting SOC 2 requirements.
You’ll shape the infrastructure of a growing voice AI company and see your work directly affect real customer conversations. There’s room to make architectural decisions, build useful tools, and establish engineering practices that the team will rely on as we grow.