Job Description

Software Engineering Intern -- LLM Systems & Applied AI Engineering
We're looking for a Software Engineering Intern passionate about building systems that make Large Language Models (LLMs) work in the real world. You'll design and implement backend components, APIs, and pipelines that connect models to applications, enabling fine-tuning, inference, evaluation, and monitoring at scale


You'll join a fast-moving team at the intersection of software engineering and AI, where every service you build helps make intelligent systems faster, safer, and more reliable. Our environment blends Go, Python, and C++ (bonus) for high-performance backend development, paired with tools and frameworks for LLM inference and experimentation


This is a 12-week, full-time, on-site internship at our San Jose, California office. You'll work on high-impact projects that directly contribute to our mission, applying your technical skills to shape the future of responsible AI

Your Responsibilities

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Design and implement backend components to support LLM inference and evaluation Integrate model endpoints into APIs and services powering real-world applications Contribute to tooling for fine-tuning, model orchestration, and inference optimization Build monitoring and analytics layers to track model responses, latency, and reliability Collaborate with ML engineers on serving pipelines, prompt evaluation, and guardrail logic Prototype and ship features that connect models to production-grade systems

Qualifications - You Must

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Currently enrolled in a Bachelor's, Master's, or PhD program in Computer Engineering or a related field in the U.S. for the full duration of the internship Graduation expected between December 2026 - June 2027 Available for 12 weeks between May-August 2026 or June-September 2026

Preferred Qualifications

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Proficiency in Go and/or Python Strong understanding of software engineering fundamentals and API design Exposure to LLM frameworks such as Hugging Face, vLLM, or OpenAI API Interest in model fine-tuning, evaluation pipelines, and prompt optimization Familiarity with inference performance concepts such as token throughput, latency, and caching Curiosity about how backend systems bring AI models to life from request to response Self-driven mindset and eagerness to learn across AI and systems boundaries

What You'll Gain

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Hands-on experience with LLM model integration, fine-tuning, and inference systems Deep understanding of how AI models are deployed, scaled, and evaluated in production Mentorship from engineers building real-world AI infrastructure and safety systems A collaborative, fast-paced environment where you can experiment, learn, and grow quickly

Compensation:

BS: $50/hour


MS: $58/hour


PhD: $65/hour