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Lead AI Research Scientist

Workato · San Francisco, United States

External listingfull-time2 months ago

About The Role

Join Workato's AI Research Lab as a Lead AI Research Scientist. In this research-first role, you will define and execute a 24-month research vision focused on advancing enterprise AI. You will build and lead a team of applied researchers and interns, publish peer-reviewed papers, and partner with Product and Engineering teams to productionize prototypes. The ideal candidate has deep technical expertise, a strong publication record, and experience leading applied research teams.

  • Definir y ejecutar una visión de investigación de 24 meses centrada en la construcción y optimización de agentes automatizados.
  • Construir, liderar y mentorear un equipo de investigadores aplicados e internos, fomentando una cultura de excelencia técnica.
  • Publicar artículos revisados por pares y asegurar patentes que diferencien a Workato en la inteligencia artificial empresarial.
  • The ideal candidate brings deep technical expertise alongside a strong record of execution
  • Deep expertise across structured and unstructured enterprise data modalities
  • Hands-on experience with large-scale model training, transformer architectures, reinforcement learning techniques, and CUDA
  • Strong publication record in top-tier ML venues (NeurIPS, ICML, ICLR)
  • MS/PhD in Computer Science, Machine Learning, or a related field
  • This role is onsite in our SF Office
  • Proven track record of transitioning research into production within large-scale systems
  • Hands-on experience with PyTorch or JAX and modern LLM frameworks
  • 5+ years leading applied research teams
  • Ability to explain complex ideas to non-experts
  • Inspirational leader who attracts, mentors, and retains top talent
  • Clear communicator able to translate complex research into business value
  • Experience operating in high-growth startup environments
  • Collaborative mindset; thrives at the intersection of research and product
  • Comfortable making decisions in a fast-moving, on-site lab environment

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