Software Engineer, AI Reliability (AI)

TL;DR

Software Engineer, AI Reliability (AI): Improving reliability across critical AI serving paths from SDK through network, API layers, serving infrastructure, and accelerators, with an accent on designing and implementing monitoring, high-availability infrastructure, and leading incident response for large language model systems. Focus on systematic improvements, understanding system composition, and ensuring Claude's reliability for users.

Location: Hybrid in San Francisco, New York City, or Seattle (USA). Visa sponsorship available.

Salary: $325,000 – $485,000 USD annually

Company

hirify.global is a public benefit corporation focused on creating reliable, interpretable, and steerable AI systems.

What you will do

• Develop appropriate Service Level Objectives for large language model serving systems.
• Design and implement monitoring and observability systems across the token path.
• Assist in the design and implementation of high-availability serving infrastructure across multiple regions and cloud providers.
• Lead incident response for critical AI services, ensuring rapid recovery, thorough incident reviews, and systematic improvements.
• Support the reliability of safeguard model serving, critical for both site reliability and hirify.global's safety commitments.

Requirements

• At least a Bachelor's degree in a related field or equivalent experience.
• Strong distributed systems, infrastructure, or reliability backgrounds (SREs).
• Curiosity and comfort jumping into unfamiliar systems during an incident to drive resolution.
• Holistic thinking about how systems compose and where the seams are.
• Excellent communication and collaboration skills to partner across the entire company.
• Care about users and feel ownership over outcomes, even for systems you don't own.

Nice to have

• Experience as an SRE, Production Engineer, or in similar reliability-focused roles on large scale systems.
• Experience operating large-scale model serving or training infrastructure (>1000 GPUs).
• Experience with one or more ML hardware accelerators (GPUs, TPUs, Trainium).
• Understanding of ML-specific networking optimizations like RDMA and InfiniBand.
• Expertise in AI-specific observability tools and frameworks.
• Experience with chaos engineering and systematic resilience testing.

Culture & Benefits

• Hybrid work policy: Expectation to be in one of our offices at least 25% of the time.
• Competitive compensation and benefits, optional equity donation matching.
• Generous vacation and parental leave, flexible working hours.
• Collaborative environment focused on advancing long-term goals of steerable, trustworthy AI.
• Lovely office space in San Francisco to collaborate with colleagues.