Intern, AI Engineering at Workato

<div class="content-intro"><h1><span style="font-family: helvetica, arial, sans-serif;"><strong>About Workato</strong></span></h1> <p>Workato delivers enterprise infrastructure for the agentic era, redefining iPaaS and helping enterprises unify data, applications, processes, and AI into a single, governed platform. A leader in Enterprise MCP and trusted by 50% of the Fortune 500, Workato’s cloud-native architecture connects every application, data source, and process to power real-time orchestration at scale. With enterprise-grade security and continuous innovation at its core, Workato provides the trusted foundation for organizations to automate with confidence and operationalize AI across the business. To learn more, visit <span><a href="http://www.workato.com" target="_blank">www.workato.com</a></span></p> <h1><strong>Why join us?</strong></h1> <p><span style="font-weight: 400;">Ultimately, Workato believes in fostering a </span><strong>flexible, trust-oriented culture that empowers everyone to take full ownership of their roles</strong><span style="font-weight: 400;">. We are driven by </span><strong>innovation </strong><span style="font-weight: 400;">and looking for</span><strong> team players </strong><span style="font-weight: 400;">who want to actively build our company.&nbsp;</span></p> <p><span style="font-weight: 400;">But, we also believe in </span><strong>balancing productivity with self-care</strong><span style="font-weight: 400;">. That’s why we offer all of our employees a vibrant and dynamic work environment </span><a href="http://www.workato.com/careers"><span style="font-weight: 400;">along with a multitude of benefits</span></a><span style="font-weight: 400;"> they can enjoy inside and outside of their work lives.&nbsp;</span></p> <p><span style="font-weight: 400;">If this sounds right up your alley, please submit an application. We look forward to getting to know you!</span></p> <p><span style="font-weight: 400;">Also, feel free to check out why:</span></p> <ul> <li style="font-weight: 400;"> <p><a href="https://www.businessinsider.com/47-enterprise-startups-to-bet-your-career-on-in-2020-2019-12"><span style="font-weight: 400;">Business Insider</span></a><span style="font-weight: 400;"> named us an “enterprise startup to bet your career on”</span></p> </li> <li style="font-weight: 400;"> <p><a href="https://www.forbes.com/cloud100/#a57477b5f941"><span style="font-weight: 400;">Forbes’ Cloud 100</span></a><span style="font-weight: 400;"> recognized us as one of the top 100 private cloud companies in the world</span></p> </li> <li style="font-weight: 400;"> <p><a href="https://www2.deloitte.com/us/en/pages/technology-media-and-telecommunications/articles/fast500-winners.html"><span style="font-weight: 400;">Deloitte Tech Fast 500</span></a><span style="font-weight: 400;"> ranked us as the 17th fastest growing tech company in the Bay Area, and 96th in North America</span></p> </li> <li> <p><a href="https://qz.com/work/2053446/the-best-companies-for-working-from-home/"><span style="font-weight: 400;">Quartz</span></a><span style="font-weight: 400;"> ranked us the #1 best company for remote workers</span></p> </li> </ul></div><h1><strong>Workato AI Lab</strong></h1> <h3><strong>About Workato AI Lab</strong></h3> <p>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.</p> <h1>Responsibilities</h1> <p>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. <br><br><span style="text-decoration: underline;"><strong>We are now filling intern positions for Winter 2026 and Spring 2027.&nbsp;</strong></span></p> <p><strong>Research Areas</strong></p> <ul> <li> <p><strong>LLM Agent Systems</strong>: Design and implement intelligent agent architectures for complex enterprise automation tasks, including multi-agent collaboration, MCP, and reasoning frameworks</p> </li> <li> <p><strong>Efficient LLM Fine-tuning</strong>: Develop novel methods for parameter-efficient adaptation, alignment, and reinforcement learning for large language models</p> </li> <li> <p><strong>High-Performance LLM Inference</strong>: Optimize inference pipelines through systems-level innovations, kernel development, and deployment strategies</p> </li> </ul> <h4>In this role, you will also be responsible to:</h4> <ul> <li> <p>Conduct original research on LLM agent architectures and optimization techniques</p> </li> <li> <p>Develop and evaluate novel algorithms with both academic rigor and production feasibility</p> </li> <li> <p>Present your work at internal research seminars and external conferences</p> </li> <li> <p>Mentor and collaborate with LLM&nbsp; engineers on implementation and deployment</p> </li> </ul> <h1><strong>Requirements</strong></h1> <h3><strong>Qualifications / Experience / Technical Skills</strong></h3> <ul> <li> <p>Currently pursuing MS/PhD in Computer Science, Machine Learning, Natural Language Processing, or related fields</p> </li> <li> <p>Publications at top-tier venues (ICML, NeurIPS, ICLR, ACL, EMNLP, NAACL)</p> </li> <li> <p>Strong programming skills in Python and PyTorch</p> </li> <li> <p>Ability to work in-person at our San Francisco office</p> </li> <li> <p>Ability to work independently and collaborate across research and engineering teams</p> </li> </ul> <h3><strong>Preferred:</strong></h3> <ul> <li> <p>Experience with self-evolving agent systems</p> </li> <li> <p>Proficiency in CUDA programming and custom kernel development for LLM operations</p> </li> <li> <p>Background in reinforcement learning-based LLM fine-tuning&nbsp;</p> </li> <li> <p>Track record of contributions to production inference systems such as vLLM, TensorRT-LLM, SGLang, or Hugging Face ecosystem</p> </li> <li> <p>Experience bridging academic research with production systems</p> </li> <li> <p>Open-source contributions to widely-used ML infrastructure projects</p> </li> </ul> <p><strong>(REQ ID: 2690)</strong></p>