About Serotiny

Inspired by life – Serotiny digitally encodes nature’s features and functions into a biologically aware API for the high-throughput, rational design of multi-domain synthetic proteins.

By coupling state-of-the-art mammalian synthetic biology and a finely-tuned software architecture, Serotiny aims to embrace one of the greatest opportunities of the 21st century to improve people’s lives. Serotiny designs custom proteins with cutting edge capabilities including treatment of cancers and genetic disorders. We partner with leaders in the life sciences to give proteins wholly new functions for more effective, safer, and customized therapies.

With support from world-class investors including Nanodimension and 8VC, Serotiny is proud to build a cross-disciplinary team of passionate individuals with varied experiences.

Software Engineer - Back-End
San Francisco, CA, Full Time

Description:

This person will lead the development Serotiny’s biologically-aware API, collaborating with biologists to deploy novel creative algorithms useful in the design of new proteins.

The digital infrastructure is part of a design/build/test loop that couples a finely tuned software architecture with wet-lab mammalian synthetic biology. Serotiny is looking for our first backend developer who is excited to design novel software architectures for synthetic biology.

Role:

  • Collaborate with biologists to define and design a custom biologically-aware API
  • Explore machine-learning & other constraint solving strategies on novel data structures
  • We currently work in Go and Ember

Qualifications:

  • BS in CS and 3+ years professional industry experience
  • Experience with Go
  • Experience with API integration
  • Experience shipping a digital product
  • Flexibility and desire to work at the early stages of a startup

Perks:

  • Full health benefits
  • Budget to host and attend developer events
  • Opportunities to develop and publish open-source standards for synthetic biology
  • Opportunities to architect and deploy novel data structures for use in biology