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San Francisco, California, United States
Remote only
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
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:
Business Insider named us an “enterprise startup to bet your career on”
Forbes’ Cloud 100 recognized us as one of the top 100 private cloud companies in the world
Deloitte Tech Fast 500 ranked us as the 17th fastest growing tech company in the Bay Area, and 96th in North America
Quartz ranked us the #1 best company for remote workers
We are looking for an exceptional AI Research Scientist to join our growing team. In this role, you will be responsible to:
Explore workflow‑aware planning, cost‑optimised RAG, tool‑use evaluation, and multi‑agent collaboration.
Prototype and benchmark models; present findings internally and externally.
Collaborate with Product & Engineering to ship research‑backed features.
Contribute to open‑source repos and author technical blogs.
Requirements
MS/PhD in Computer Science, ML, or related field—or equivalent experience.
Hands‑on with PyTorch/JAX and modern LLM frameworks.
Strong publication record in top-tier ML venues (NeurIPS, ICML, ICLR) or demonstrable impactful experimental work; hands‑on experience with large‑scale model training, transformer architectures, reinforcement‑learning techniques, and CUDA.
Curiosity‑driven and self‑directed.
Ability to explain complex ideas to non‑experts.
Team player who welcomes feedback and knowledge‑sharing.
