Backend Software Engineer (AI)

TL;DR

Backend Software Engineer (AI): Architecting and scaling infrastructure that powers knowledge systems with an accent on designing robust systems and backend services. Focus on integrating and structuring data across internal platforms, transforming it into formats optimized for use by downstream systems and AI workflows.

Location: San Francisco or Seattle, USA

Salary: $230,000 – $385,000 + Offers Equity

Company

hirify.global is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity.

What you will do

• Design, build, and maintain backend services and APIs to support intelligent automation and knowledge systems.
• Integrate and structure data across internal platforms, transforming it into formats optimized for use by downstream systems and AI workflows.
• Collaborate closely with product, research, and engineering teams to integrate hirify.global models into high-leverage workflows.
• Own the full development lifecycle of new backend systems and internal platform capabilities.
• Build with scale and maintainability in mind, while rapidly iterating on new ideas.

Requirements

• 4+ years of backend engineering experience at product-driven companies (excluding internships).
• Proficiency in backend technologies including Python, FastAPI, and Postgres.
• Experience designing and scaling distributed systems, APIs, or data processing pipelines.
• A pragmatic mindset, comfortable shipping iteratively while building toward a long-term vision.
• An interest in structured knowledge representation, internal search, agent infrastructure, or systems that evolve over time.
• Curiosity about AI/ML and excitement to work alongside world-class research and product teams (hands-on experience is a bonus, not a must).

Culture & Benefits

• Self-contained multi-disciplinary team with fast feedback loops with customers.
• Opportunity to use hirify.global technologies and build systems that demonstrate the leverage possible with well-designed AI implementations.
• Focus on building simple primitives to apply world-class knowledge to many domains.