Software Engineer (AI Infrastructure)

TL;DR

Software Engineer (AI Infrastructure): Design, build, and improve AI/ML systems and data pipelines infrastructure, including platforms that power large language model serving, routing, and orchestration at scale. Focus on building agentic systems that leverage AI to proactively monitor, diagnose, debug and maintain infrastructure health at scale across cloud deployments.

Location: Hybrid (3-4 days a week in one of our Bay Area offices)

Salary: $175,000 - $270,000 annually

Company

hirify.global is the Work AI platform that helps everyone work smarter with AI, powering intelligent Search, an AI Assistant, and scalable AI agents on a secure, open platform.

What you will do

• Design, build, and improve AI/ML systems and data pipelines infrastructure for LLM serving, routing, and orchestration at scale.
• Work with and enable other engineers focused on modeling and agentic capabilities.
• Build agentic systems that leverage AI to proactively monitor, diagnose, debug, and maintain infrastructure health across cloud deployments.
• Write robust, readable, maintainable, and testable code.
• Mentor junior engineers or learn from experienced ones.

Requirements

• 2-5 years of experience
• BA/BS in computer science, math, sciences, or related degree
• Proven ability to design, build, and ship production-ready software, ideally around AI/ML infrastructure (batch processing pipelines, serving infrastructure, LLM orchestration)
• Strong coding skills (Python, Go, Java)
• Thrive in customer-focused, tight-knit, cross-functional environment as a team player
• Proactive and positive attitude to lead, learn, troubleshoot, and take ownership

Culture & Benefits

• Comprehensive benefits including Medical, Vision, Dental coverage, generous time-off, 401k contributions
• Home office improvement stipend, annual education and wellness stipends
• Vibrant company culture with regular events and healthy lunches daily
• Commitment to diversity, inclusion, and AI fluency for all hires

Hiring process

• AI-focused exercise or discussion in interviews to understand AI thinking and usage