Workato transforms technology complexity into business opportunity. As the leader in enterprise orchestration, Workato helps businesses globally streamline operations by connecting data, processes, applications, and experiences. Its AI-powered platform enables teams to navigate complex workflows in real-time, driving efficiency and agility.
Trusted by a community of 400,000 global customers, Workato empowers organizations of every size to unlock new value and lead in today’s fast-changing world. Learn how Workato helps businesses of all sizes achieve more at 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!
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 Data Scientist to join our growing team. In this role, y ou 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.
Ability to explain complex ideas to non‑experts.
Team player who welcomes feedback and knowledge‑sharing.
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