Intern, AI Engineering

Workato AI Lab is at the forefront of enterprise AI innovation, developing cutting-edge agentic systems that transform how businesses automate and optimize their workflows. Our team bridges academic research with real-world applications, creating AI systems that serve millions of users across global enterprises.

We are seeking exceptional graduate students to join our AI Lab as Research Interns in San Francisco. You'll work on fundamental problems in LLM-based agentic systems and efficient AI infrastructure, with opportunities to publish your research while making direct impact on production systems serving enterprise customers.
We are now filling intern positions for Winter 2026 and Spring 2027. 

Research Areas

• LLM Agent Systems: Design and implement intelligent agent architectures for complex enterprise automation tasks, including multi-agent collaboration, MCP, and reasoning frameworks

• Efficient LLM Fine-tuning: Develop novel methods for parameter-efficient adaptation, alignment, and reinforcement learning for large language models

• High-Performance LLM Inference: Optimize inference pipelines through systems-level innovations, kernel development, and deployment strategies

• Conduct original research on LLM agent architectures and optimization techniques

• Develop and evaluate novel algorithms with both academic rigor and production feasibility

• Present your work at internal research seminars and external conferences

• Mentor and collaborate with LLM  engineers on implementation and deployment

• Currently pursuing MS/PhD in Computer Science, Machine Learning, Natural Language Processing, or related fields

• Publications at top-tier venues (ICML, NeurIPS, ICLR, ACL, EMNLP, NAACL)

• Strong programming skills in Python and PyTorch

• Ability to work in-person at our San Francisco office

• Ability to work independently and collaborate across research and engineering teams

• Experience with self-evolving agent systems

• Proficiency in CUDA programming and custom kernel development for LLM operations

• Background in reinforcement learning-based LLM fine-tuning 

• Track record of contributions to production inference systems such as vLLM, TensorRT-LLM, SGLang, or Hugging Face ecosystem

• Experience bridging academic research with production systems

• Open-source contributions to widely-used ML infrastructure projects

(REQ ID: 2690)