AI Infrastructure Engineer
At a glance
Mid-level ML Platform & Ops role at Palona AI. Los Altos · full-time · no salary stated · we estimate $144,980–$225,625 across the United States.
Palona AI has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Build and maintain cloud foundations for real-time AI services and customer-facing products
- Set reliability targets, improve monitoring, plan capacity and test failures to prevent incidents
- Create safer release workflows and reusable infrastructure patterns across development, staging and production
- Trace failures across applications, networks, databases, model providers and outside integrations
- Lower infrastructure and model-serving costs while strengthening security and helping engineers ship with less manual work
What you bring
- At least three years of industry experience in a relevant technical field
- You have built or run production distributed systems and can write dependable automation in Python or another modern language
- Hands-on experience with a major cloud platform, plus containers, infrastructure as code, CI/CD and production monitoring
- You can diagnose unclear operational problems and carry fixes through to lasting improvements
Who this fits
This suits a software-minded infrastructure engineer who wants to build and operate production systems rather than handle ticket-driven IT work. The role is based onsite in Los Altos.
From the employer
Palona’s AI agents operate continuously in production, handle real-time guest interactions, integrate with restaurant systems, and face sharp traffic peaks. Infrastructure is therefore part of the product: latency, reliability, deployment safety, observability, security, and cost directly shape the guest and operator experience.
We are looking for an Infrastructure Engineer who combines cloud and reliability depth with strong software engineering judgment. You will build and operate the platform beneath Palona’s AI products, improve how engineers ship, and turn production signals into durable system improvements. This is not a ticket-driven IT or operations role. You will write production code, design systems, automate repetitive work, and own outcomes across the full service lifecycle.
Our current environment includes Python services, Docker, AWS and selected Azure services, ECS and Lambda workloads, API Gateway, load balancers, relational data systems, OpenTofu/Terraform, Datadog, and CI/CD automation. We value the ability to learn and make sound tradeoffs more than exact tool-for-tool matching.
What you will own:
- Design, build, and evolve secure, scalable cloud infrastructure for real-time AI services and customer-facing applications.
- Improve service reliability through clear SLOs, actionable observability, capacity planning, failure testing, and pragmatic incident prevention.
- Build deployment and release systems that make production changes fast, repeatable, auditable, and safe.
- Own infrastructure as code, environment consistency, and reusable platform patterns across development, staging, and production.
- Partner with product and AI engineers on architecture, performance, data flows, and operational readiness for new capabilities.
- Diagnose complex distributed-system failures across application, network, database, model-provider, and third-party integration boundaries.
- Reduce infrastructure and model-serving cost without compromising customer experience or engineering velocity.
- Strengthen secrets management, access controls, backup and recovery, vulnerability management, and other practical security foundations.
- Build internal tooling and paved paths that let engineers ship and operate services with less manual work.
- Participate in incident response and turn incidents into better systems, automation, documentation, and engineering judgment.
Requirements
- 3+ years industrial experience in relevant technical domain.
- Strong software engineering fundamentals and experience building or operating production distributed systems.
- Hands-on experience with a major cloud platform; AWS experience is especially relevant.
- Experience with containers, infrastructure as code, CI/CD, monitoring, alerting, and production debugging.
- Ability to write reliable automation and services in Python or another modern programming language.
- Sound judgment around availability, latency, scalability, security, and cost tradeoffs.
- A track record of taking ambiguous operational problems from diagnosis through durable resolution.
- Clear communication during architecture reviews, launches, and incidents.
- AI-native working habits and curiosity about the operational behavior of LLM- and agent-powered systems.
Benefits
- Competitive Salary and Stock Option Plan.
- Medical, dental, vision, retirement, leave, and disability benefits as applicable.
- Family Leave
- Short Term & Long Term Disability
- Paid time off and company holidays.
- Learning and development support.