Member of Technical Staff, Infrastructure

Intent Lab Inc; Sunnyvale (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Intent Lab Inc. in Sunnyvale, CA, seeks a highly capable Member of Technical Staff (Infrastructure) to design, build, and scale core systems powering our AI-driven software platform.

You will own distributed services, cloud deployment, and observability while collaborating with AI teams to operationalize models at scale. You will emphasize reliability, performance, and cost optimization, delivering secure CI/CD pipelines and infrastructure that supports model inference, training, and evaluation

Qualifications

• 3+ years of backend or infrastructure engineering experience.
• Strong understanding of distributed systems, networking, and cloud architecture.
• Proficiency in Go, Python, or Rust.
• Experience with Docker, Kubernetes, and infrastructure-as-code tools (Terraform, Pulumi).
• Familiarity with CI/CD, observability tools, and cloud-native design patterns.

Responsibilities

• Design, build, and maintain distributed systems powering AI platform & dev tools.
• Architect and scale high-throughput, low-latency backend services.
• Optimize performance, reliability, and cost across the infrastructure.
• Operate and improve cloud-native environments (AWS, GCP, Azure).
• Build automation for deployment, provisioning, and environment lifecycle management.
• Implement secure, scalable CI/CD pipelines.
• Support infrastructure for model inference, training, and evaluation pipelines.
• Collaborate with AI teams to operationalize models at scale.
• Establish observability standards and drive incident response.
• Partner with AI, product, and engineering to unblock delivery.

Skills

Backend engineering

Distributed systems

Go

Python

Rust

Docker

Kubernetes

Terraform

Pulumi

CI/CD

Cloud architecture

Observability

Ambiguity tolerance

Tools

Docker

Kubernetes

Terraform

Pulumi

Job description

We are looking for highly capable and execution-focused Member of Technical Staff (Infrastructure) to build and scale the core systems powering AI-driven software development. This role requires strong systems thinking, deep infrastructure expertise, and the ability to operate in a fast-paced, ambiguous environment.

Key Responsibilities

Core Infrastructure

• Design, build, and maintain distributed systems that power our AI platform and developer tools.

• Architect and scale high-throughput, low-latency backend services.

• Optimize performance, reliability, and cost across the infrastructure stack.

Cloud & Deployment Systems

• Operate and improve cloud-native environments (AWS, GCP, or Azure).

• Build automation for deployment, provisioning, and environment lifecycle management.

• Implement secure, scalable, and repeatable CI/CD pipelines.

AI Infrastructure

• Support infrastructure for model inference, training, and evaluation pipelines.

• Work closely with AI teams to operationalize models at scale.

• Build systems for prompt orchestration, agent execution, and developer tool integration.

Reliability & Observability

• Establish observability standards (metrics, logging, tracing).

• Drive incident response, debugging, and reliability engineering practices.

• Contribute to security, compliance, and data privacy best practices.

Collaboration

• Partner across AI, product, and engineering teams to unblock delivery.

• Contribute to technical planning, architecture reviews, and infrastructure strategy.

Qualifications

• 3+ years of experience in backend or infrastructure engineering.

• Strong understanding of distributed systems, networking, and cloud architecture.

• Proficiency in Go, Python, or Rust.

• Experience with Docker, Kubernetes, and infrastructure-as-code tools (Terraform, Pulumi).

• Familiarity with CI/CD, observability tools, and cloud-native design patterns.

• Ability to operate independently and deliver under ambiguity.

What We Value

• Ownership and accountability across the full infrastructure stack.

• Pragmatism over over-engineering.

• Speed without compromising reliability.

• Curiosity about AI systems and their infrastructure implications.