Infrastructure and Platform Engineer (Metal)

TL;DR

Infrastructure and Platform Engineer (Metal): Designing and operating Kubernetes-based platforms for workload orchestration and hardware allocation in large-scale AI systems with an accent on cluster lifecycle, scaling, and integration with CI/CD pipelines. Focus on building APIs, platform services, and operational maturity to support internal development and customer workloads on custom accelerator hardware.

Location: Hybrid based out of Santa Clara, CA; Austin, TX; or Toronto, ON. Offer contingent upon eligibility to access U.S. export-controlled technology; compliance with U.S. Export Administration Regulations required, may affect nationals of certain countries.

Salary: $100k - $500k (base + variable, depending on experience, skills, location)

Company

hirify.global leads in cutting-edge AI technology, developing high-performance RISC-V CPUs and AI platforms unifying software, compilers, networking, and semiconductors.

What you will do

• Design and build platform services for workload orchestration, ML services, and internal development workflows.
• Develop APIs and systems for user and service interaction with infrastructure platforms.
• Own Kubernetes-based platforms, including cluster lifecycle, scaling, and operational maturity.
• Integrate platform systems with CI/CD pipelines, GitOps workflows, and internal tooling.
• Partner with SRE, infrastructure, and deployment teams to support large-scale environments.

Requirements

• Eligible to access U.S. export-controlled technology per U.S. EAR; citizenship/permanent residency or license approval may be required.
• Experienced backend or infrastructure engineer focused on platform development in large-scale environments.
• Strong expertise in Kubernetes (cluster provisioning, operators, production debugging).
• Proficient in Python or Go for APIs and platform services.
• Comfortable with Linux systems, networking fundamentals, and distributed systems.
• Collaborative and adaptable across engineering, infrastructure, and deployment teams.

What You Will Learn

• Large-scale AI platforms on custom accelerator hardware.
• Advanced Kubernetes patterns (operators, controllers, cluster lifecycle).
• Evolution of internal platforms to production-grade customer systems.
• Orchestration, APIs, and infrastructure for AI workloads.
• Scaling platform engineering across on-prem and hybrid environments.

Culture & Benefits

• Value collaboration, curiosity, and commitment to solving hard problems.
• Highly competitive compensation package and benefits.
• Equal opportunity employer.