Forward Deployed Engineer Intern (AI)

Location: Hybrid in San Mateo, United States

Company

hirify.global provides model APIs, dedicated inference infrastructure, and agent sandbox environments for building, deploying, and scaling production AI applications.

What you will do

• Work directly with customers building AI products and agents.
• Integrate model APIs, dedicated inference endpoints, and agent sandbox environments.
• Reproduce and debug issues involving APIs, networking, containers, Linux, and inference workloads.
• Investigate latency, throughput, concurrency, reliability, and other production issues.
• Build demos, integrations, scripts, proofs of concept, reference implementations, documentation, runbooks, and internal tooling.
• Partner with product, engineering, SRE, and go-to-market teams to resolve customer issues and identify product improvements.

Requirements

• Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related technical field.
• Strong programming fundamentals and proficiency in Python or another general-purpose programming language.
• Familiarity with Linux and APIs, along with strong debugging and problem-solving ability.
• Ability to communicate directly with customers and explain technical concepts clearly.
• High ownership and willingness to work through ambiguous problems.
• Availability for approximately 20 hours per week during the academic term or full-time during summer.

Nice to have

• Experience building applications with LLM APIs.
• Familiarity with Docker, Kubernetes, cloud infrastructure, agent frameworks, coding agents, vLLM, SGLang, Ray, or other inference systems.
• Understanding of latency, throughput, caching, batching, and concurrency in inference systems.
• Hackathon projects, open-source contributions, startup experience, teaching, consulting, developer relations, or technical user support experience.
• Professional working proficiency in Mandarin Chinese.

Culture & Benefits

• Work on real customer deployments and production-facing AI infrastructure problems.
• Develop practical experience with modern AI infrastructure and customer-driven engineering.
• Build reusable tools and documentation used by future customers.
• Learn how engineering decisions influence customer adoption and business outcomes.