Founding ML Infrastructure Engineer, Delphina

• Hands on experience with ML systems, either in model building or large scale training
• 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

Desirable

• 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 looking for an experienced ML Infrastructure Engineer to join as a founding team member 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
• 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