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.