About Zuma

Zuma makes an automated sales agent that converses with 100% of inbound leads, ultimately improving the way consumers interact with businesses and organizations. We’ve built this from the ground up using AI, ML, and human support which helps increase sales conversion and support capacity for businesses of all kinds. Zuma is one of the fastest-growing startups in San Francisco, and is well-funded and backed by world-class investors such as Andreessen Horowitz (a16z), Y-Combinator, Joe Montana’s fund (Liquid 2 Ventures), Day One Ventures, Soma Capital, and other notable angel investors including Austen Allred (from Lambda School), YC’s former-COO Qasar Younis, among others.

Headquartered in San Francisco, USA, we operate nationally and have plans to grow rapidly over the next few years. To do that, we need great people committed to our vision in a big way. We're looking to build a team of rockstars that are equally excited about the opportunity to leverage technology to improve the way customers interact with businesses!


  • 3+ years of experience
  • Strong engineering skills: experience in OOP, Design Patterns, and time and space-efficient algorithms
  • Prior experience building solutions on a public cloud (eg: AWS, Azure, GCP)
  • Prior experience working on NLP/NLU solutions from model training to deployment
  • Excellent working knowledge of Python, deep learning frameworks (pytorch, tensorflow), and experience working with Data Science packages within python
  • Working knowledge of containerization and microservices based solution architecture
  • Solid understanding of Machine and Deep Learning algorithms and techniques (transformer based architectures, different types of embedding models)
  • Great team player, willing to wear many hats
  • Flexibility to work across timezones


You will:

  • Work across engineering, operations, and AI teams to build and scale practical Machine Learning solutions which will improve the intelligence of Kelsey AI
  • Train and maintain our deep learning models and define conversational flows for our chatbot. This will involve all steps of the data science lifecycle from data collection to model tuning.