Job Specifications
Deep Learning Intern – LLM Research (Safety & Alignment)
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
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
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
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
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
About the Company
A10 Networks provides security and infrastructure solutions for on-premises, hybrid cloud, and edge-cloud environments. Our 7000+ customers span global large enterprises and communications, cloud and web service providers who must ensure business-critical applications and networks are secure, available, and efficient. Founded in 2004, A10 Networks is based in San Jose, Calif. and serves customers globally. For more information, visit A10networks.com.
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