About Delphina

Today’s Data Scientists are in pain - spending their time manually wrangling data, building models through slow trial and error, taking on painstaking rewrites for deployment, and dealing with countless other frustrating bottlenecks. And the tools they are using for much of this work – e.g. Jupyter notebooks and Pandas – are over a decade old.
We founded Delphina to change this: our mission is to help the world get better at using data to understand the present and predict the future. Delphina is an AI Agent for Data Science: leveraging a combination of generative AI, large scale optimization, and specialized infrastructure to automate the time-consuming but necessary tasks to build powerful ML models quickly; Delphina will identify relevant data, clean it, train models, and even productionize pipelines.
Our team has previously led large data science and machine learning teams (covering both applications and infrastructure), built startups, and created successful tools for enterprise ML.
We're backed by top AI investors, including Fei-Fei Li, Radical VC, and Costanoa VC.

What you’ll do

We're looking for an experienced ML Infrastructure Engineer to join as a Member of our Technical Staff of Delphina.
As one of our key early hires, you will partner closely with our early team on the direction of our product and drive critical technical decisions. You will have broad impact over the technology, product, and our company's culture.
You will be responsible for:
- Developing platforms that enable scientists, researchers, developers to run ML jobs easily and quickly at scale using the latest technologies
- Developing solutions that will orchestrate and support massive quantities of data through stages like ingestion, indexing/mining, transformation, machine learning, online deployment
- Defining a consistent continuous integration/deployment model that will encourage cross-functional development teams to self-service application unit testing, deployment and operations
- Influencing and lead cross-functional initiatives that will align the team towards commonly used technologies and methodologies

Proficiency in multiple programming languages relevant for such systems (e.g. Python, Rust, C++, Go, Java)
Knowledge about what it takes to deploy and operate high availability production systems in the cloud
Experience designing service-oriented architectures and leveraging various data store technologies
Energy and ambition to build a product that is surprisingly good in surprising ways
Intrinsic desire to always be improving our product and yourself. Growth mindset to both stay ahead of the curve and pick up whatever knowledge you're missing to get the job done

Experience working directly on machine learning models – either by partnering with scientists and engineers who are building models, or by building models yourself
Experience leading cross functional teams through ambiguous problems