Staff Infrastructure Engineer (AI)

TL;DR

Staff Infrastructure Engineer (AI): Designing and implementing large-scale infrastructure systems to support AI scientist training, evaluation, and deployment with an accent on distributed environments, performance optimization, and container orchestration. Focus on building scalable VM/sandboxing architectures, optimizing data pipelines, and enabling stable reinforcement learning workflows.

Location: Hybrid with at least 25% office presence in San Francisco, USA

Salary: $340,000 - $425,000 USD annually

Company

hirify.global is a public benefit corporation focused on creating reliable, interpretable, and steerable AI systems that are safe and beneficial for society.

What you will do

• Design and implement large-scale infrastructure for AI scientist training, evaluation, and deployment across distributed systems
• Identify and resolve infrastructure bottlenecks impacting scientific AGI progress
• Develop robust evaluation frameworks for scientific AGI measurement
• Build scalable VM/sandbox/container architectures for safe execution of long-horizon AI tasks
• Collaborate to translate experimental requirements into production-ready infrastructure
• Develop and optimize large-scale data pipelines and reinforcement learning training/inference workflows

Requirements

• Must have 6+ years of experience in infrastructure engineering with expertise in large-scale distributed systems
• Strong communication and collaboration skills
• Deep knowledge of performance optimization and system architectures for high-throughput ML workloads
• Experience with containerization (Docker, Kubernetes) and orchestration at scale
• Proven track record building large-scale data pipelines and distributed storage systems
• Ability to diagnose and resolve complex infrastructure challenges in production
• Experience working across the full ML stack from data pipelines to performance optimization
• Experience collaborating with researchers to scale experimental ideas

Nice to have

• Experience with language model training infrastructure and distributed ML frameworks (PyTorch, JAX)
• Background in AI research lab infrastructure or large-scale ML organizations
• Knowledge of GPU/TPU architectures and language model inference optimization
• Experience with cloud platforms (AWS, GCP) at enterprise scale
• Familiarity with VM and container orchestration
• Experience with workflow orchestration and experiment management systems
• History working with large-scale reinforcement learning
• Comfort with large-scale data pipelines (Beam, Spark, Dask)

Culture & Benefits

• Competitive compensation including equity and benefits
• Generous vacation and parental leave
• Flexible working hours and hybrid work policy
• Visa sponsorship available with immigration lawyer support
• Collaborative and impact-driven research environment

Hiring process

• Evaluation of relevant experience and skills
• Technical interviews focusing on infrastructure and distributed systems
• Assessment of communication and collaboration abilities