Description
About Workato
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 www.workato.com
Why join us?
Ultimately, Workato believes in fostering a flexible, trust-oriented culture that empowers everyone to take full ownership of their roles. We are driven by innovation and looking for team players who want to actively build our company.
But, we also believe in balancing productivity with self-care. That’s why we offer all of our employees a vibrant and dynamic work environment along with a multitude of benefits they can enjoy inside and outside of their work lives.
If this sounds right up your alley, please submit an application. We look forward to getting to know you!
Also, feel free to check out why:
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Business Insider named us an “enterprise startup to bet your career on”
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Forbes’ Cloud 100 recognized us as one of the top 100 private cloud companies in the world
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Deloitte Tech Fast 500 ranked us as the 17th fastest growing tech company in the Bay Area, and 96th in North America
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Quartz ranked us the #1 best company for remote workers
Workato AI Lab
About Workato AI Lab
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.
Responsibilities
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
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LLM Agent Systems: Design and implement intelligent agent architectures for complex enterprise automation tasks, including multi-agent collaboration, MCP, and reasoning frameworks
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Efficient LLM Fine-tuning: Develop novel methods for parameter-efficient adaptation, alignment, and reinforcement learning for large language models
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High-Performance LLM Inference: Optimize inference pipelines through systems-level innovations, kernel development, and deployment strategies
In this role, you will also be responsible to:
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Conduct original research on LLM agent architectures and optimization techniques
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Develop and evaluate novel algorithms with both academic rigor and production feasibility
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Present your work at internal research seminars and external conferences
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Mentor and collaborate with LLM engineers on implementation and deployment
Requirements
Qualifications / Experience / Technical Skills
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Currently pursuing MS/PhD in Computer Science, Machine Learning, Natural Language Processing, or related fields
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Publications at top-tier venues (ICML, NeurIPS, ICLR, ACL, EMNLP, NAACL)
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Strong programming skills in Python and PyTorch
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Ability to work in-person at our San Francisco office
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Ability to work independently and collaborate across research and engineering teams
Preferred:
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Experience with self-evolving agent systems
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Proficiency in CUDA programming and custom kernel development for LLM operations
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Background in reinforcement learning-based LLM fine-tuning
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Track record of contributions to production inference systems such as vLLM, TensorRT-LLM, SGLang, or Hugging Face ecosystem
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Experience bridging academic research with production systems
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Open-source contributions to widely-used ML infrastructure projects
(REQ ID: 2690)
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