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