Soteris (S19)
We write machine learning software to more accurately price insurance.

Software Engineer (Data Infrastructure Focus) - Employee #4

San Francisco
3+ years
About Soteris

Soteris writes machine learning software to more accurately price insurance. We’re currently a team of four and we’re looking to add employees #4 and #5 to scale up the team. We’re YC-backed with over $500k of revenue and we’ve raised over $4m from some fantastic investors.

About the role

Skills: Python, Linux, Machine Learning, Data Warehousing

Soteris is a YC-backed company with a four-person team, a multi-year runway, and almost $1mm of contract revenue, serving a massive industry.

We write machine learning software to more accurately price insurance. As a Software Engineer for Data Infrastructure, you will be the fifth team member, working alongside a passionate team with diverse backgrounds working hard to modernize an antiquated industry. You will be responsible for the core infrastructure we use to do everything from onboarding customer datasets, to processing of that data for use by our ML models, to creation of our training pipelines to allow our ML system to iteratively become smarter, to deploying our ML models' scores and integrating those scores into customer workflows. Additionally, while your focus will be on data infrastructure, as part of a small, agile team, you’ll have the opportunity to work on multiple projects across the company to make an outsized impact on our product as a whole.


I founded Soteris because the way insurance rates are set results in massive inefficiencies that increase prices for policyholders like you and me. From years of experience, I know for a fact that a machine learning approach is orders of magnitude better than what the largest insurers do - and our customer list is proof of that claim.

Before Soteris, I built a $750 million property and casualty (everything that’s not life or health) insurance company from scratch, out of a $16 billion hedge fund called Pine River Capital. This gave me deep visibility into how insurance works, through which I saw firsthand how much of the insurance value chain is littered with processes that might have made sense in 1920 – but are way past their prime in 2020. These inefficiencies lead to increased prices for policyholders, and they can all be solved by better use of existing data.

I started Soteris to do just that. In a short time, Soteris demonstrated that we could solve these problems to the tune of almost doubling our first customer’s policy profitability. They plan to drop rates for at least 80% of their applicants as a direct result of the efficiencies Soteris’s software provides. I think that’s pretty cool.

In the end, our goal is to own the entire data value chain within property and casualty insurance. We've raised a large round from a number of top investors, including Y Combinator, Amplify Partners, Khosla Ventures, and Data Collective. Combine that with a lot of early revenue and a low burn rate, and we have a long, ample runway to execute our mission.


You will be an integral part of the small team responsible for our entire system used to ingest and process data from external sources and deploy our ML model scores back out to customer workflows.

Specifically, you would:

  • Work with clients to understand requirements, formulate use cases, and build pragmatic solutions for data ingestion and API integration.
  • Make infrastructure decisions for data processing and storage.
  • Create data pipelines to automate the onboarding and normalizing of highly heterogeneous customer data sets.
  • Work hand-in-hand with the machine learning team on managing and debugging issues with the data they need to build models.
  • Help establish the engineering best practices and collaborative engineering culture that will allow the whole team to thrive.

Currently we run on AWS and our entire stack is in Python, though you don’t need any experience in Python so long as you have the aptitude to learn it. Experience with Machine Learning product development is another nice-to-have, but it's not a necessity for those with the interest and ability to learn it.


  • Experience building data infrastructure and data pipelines.
  • Experience building and operating production services with high availability, scalability, and correctness.
  • 3+ years of professional software development experience.
  • 1+ years of experience with Linux-based systems in a production environment.
  • A strong desire to learn new things, with a high learning rate.
  • An approach to teamwork similar to ours (reach out to find out more here).
  • A deep interest in what we're working on.


Currently we run a variety of API endpoints on AWS and our entire stack is in Python, though you don’t need any experience in Python so long as you have the aptitude to learn it.

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