Job Description

Deep Learning Intern -- LLM Research & Model Safety

Deep Learning Intern - LLM Research (Safety & Alignment)

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We're seeking a

Deep Learning Intern

passionate about advancing Large Language Model (LLM) research, with a focus on safety, interpretability, and alignment. In this role, you'll investigate model behavior, identify vulnerabilities, and design fine-tuning and evaluation strategies to make AI systems more robust and trustworthy


You'll collaborate with researchers and engineers to experiment with LLMs, Vision-Language Models (VLMs), and multimodal architectures, contributing to next-generation AI systems that are both powerful and safe


This is a

12-week, full-time, on-site internship

at our

San Jose, California office

, where you'll work on high-impact projects that directly support our mission. We're looking for motivated students eager to apply their technical and research skills to shape the future of responsible AI

Your Responsibilities

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Research and prototype methods to improve safety, interpretability, and reliability of LLMs Fine-tune pre-trained LLMs on curated datasets for task adaptation and behavioral control Design evaluation frameworks to measure robustness, alignment, and harmful output rates Conduct adversarial and red-teaming experiments to uncover weaknesses in model responses Collaborate with engineering teams to integrate findings into production inference systems Explore and experiment with multimodal model extensions, including VLMs and audio-based models Stay up-to-date with the latest research on model alignment, parameter-efficient tuning, and safety benchmarks

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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Strong programming skills in Python and experience with deep learning frameworks (PyTorch or TensorFlow) Understanding of transformer architectures, attention mechanisms, and scaling laws Experience or coursework in LLM fine-tuning, LoRA/QLoRA, or instruction-tuning methods Familiarity with evaluation datasets and safety benchmarks (e.g., HELM, TruthfulQA, JailbreakBench) Interest in AI safety, interpretability, or bias detection Exposure to Vision-Language Models (VLMs), speech/audio models, or multimodal architectures is a plus Ability to implement research ideas into working prototypes efficiently

What You'll Gain

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Hands-on experience in LLM and multimodal model research, focusing on safety and performance Exposure to fine-tuning, red-teaming, and evaluation of frontier AI models Mentorship from experts working at the intersection of deep learning research and AI safety engineering Opportunities to publish internal studies or papers and contribute to real-world model safety initiatives

Compensation:

BS: $50/hour


MS: $58/hour


PhD: $65/hour