About the Company

Companies want to train their own large models on their own data. The current industry standard is to train on a random sample of your data, which is inefficient at best and actively harmful to model quality at worst. There is compelling research showing that smarter data selection can train better models faster—we know because we did much of this research. Given the high costs of training, this presents a huge market opportunity. We founded DatologyAI to translate this research into tools that enable enterprise customers to identify the right data on which to train, resulting in better models for cheaper.

Our team has pioneered deep learning data research, built startups, and created tools for enterprise ML.

We have raised over $11.5M from top-tier investors including Amplify Partners, Radical Ventures, Conviction Capital, Jeff Dean, Yann LeCun, Geoff Hinton, and Adam D’Angelo to help make our vision a reality. Learn more about the company here.

This role is based in Redwood City, CA. We are in person 5 days per week and offer relocation assistance to new employees. We provide visa sponsorship for candidates selected for this role.

About the Role

We’re looking for seasoned ML Infrastructure engineers with experience designing, building, and maintaining training infrastructure for our in-house ML research and validation efforts and the core infrastructure for running the curation pipeline that we deliver to our customers. As one of our early senior hires, you will partner closely with our founders on the direction of our product and drive business-critical technical decisions.

You will contribute to developing core infrastructure components that impact our ability to deliver, scale, and deploy our product. These are key components of our stack that allow us to process customer data and apply state-of-the-art research to identify the most informative data points in large-scale datasets. You will have a broad impact on the technology, product, and our company's culture.

As an ML Infrastructure Engineer at DatologyAI, you will be responsible for:

• Architect, build and maintain the infrastructure that ensures highly available GPU workloads for training-purposes

• Troubleshoot and resolve issues across GPU resources, networking, OS, drivers, and cloud environments, automate detection and recovery of such issues

• Design, build, and maintain the infrastructure that powers our data curation product.

• Partner with researchers and engineers to bring new features and research capabilities to our customers

• Ensure that our infrastructure and systems are reliable, secure, and worthy of our customers' trust.

About You

There are a few specific things we’ll be looking for that will help you succeed in this role:

• Have meaningful experience with leading and building production ML infrastructure and platforms that deliver on major product initiatives.

• Proficiency in Python and in the most commonly used tools in the infrastructure space: Linux, Kubernetes, Terraform / Pulumi, etc

• Strong knowledge of hardening cloud native and especially K8s workloads.

• Experience maintaining a high-quality bar for design, correctness, and testing.

• Have a humble attitude, an eagerness to help your colleagues, and a desire to do whatever it takes to make the team succeed

• Own problems end-to-end and are willing to pick up whatever knowledge you're missing to get the job done.

We would love it if candidates have:

• Experience running data-processing workloads in k8s (e.g spark on k8s)