Software Engineer, Infrastructure

ABOUT THE COMPANY Models are what they eat. But a large portion of training compute is wasted training on data that are already learned, irrelevant, or even harmful, leading to worse models that cost more to train and deploy. At DatologyAI, we’ve built a state of the art data curation suite to automatically curate and optimize petabytes of data to create the best possible training data for your models. Training on curated data can dramatically reduce training time and cost (7-40x faster training depending on the use case), dramatically increase model performance as if you had trained on >10x more raw data without increasing the cost of training, and allow smaller models with fewer than half the parameters to outperform larger models despite using far less compute at inference time, substantially reducing the cost of deployment. For more details, check out our recent research on synthetic data scaling (BeyondWeb https://www.datologyai.com/blog/beyondweb) and pretraining with domain-specific data (The Finetuner’s Fallacy https://www.datologyai.com/blog/finetuners-fallacy). We raised a total of $57.5M in two rounds, a Seed and Series A. Our investors include Felicis Ventures, Radical Ventures, Amplify Partners, Microsoft, Amazon, and AI visionaries like Geoff Hinton, Yann LeCun, Jeff Dean, and many others who deeply understand the importance and difficulty of identifying and optimizing the best possible training data for models. Our team has pioneered this frontier research area and has the deep expertise on both data research and data engineering necessary to solve this incredibly challenging problem and make data curation easy for anyone who wants to train their own model on their own data. This role is based in Redwood City, CA. We are in office 4 days a week. ABOUT THE ROLE We're looking for an experienced Infrastructure Engineer to join as a member of our core Datology AI team. 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 lead the development of our data infrastructure capabilities, including multi-cloud support and support for various deployment models, as well as training and inference infrastructure. You will have a broad impact on the technology, product, and our company's culture. WHAT YOU'LL WORK ON - Design and build the development and production platforms that power our products, enabling reliability and security at scale - Architect, build, and deploy our core infrastructure while supporting multiple cloud providers and various deployment models - Accelerate company productivity by empowering your fellow engineers & teammates with excellent tooling and systems, providing a best-in-case experience - Partner with researchers and engineers to bring new features and research capabilities to our customers ABOUT YOU - 4+ years of relevant experience - Have meaningful experience in spearheading and constructing large-scale infrastructure - Proficiency in bash, Kubernetes, Python, and/or Terraform or similar technologies - Have experience working with AWS, other cloud platforms such as Azure or GCP and/or on-prem environments - Have expertise in debugging problems across the stack, such as networking issues, performance problems, hardware issues or memory leaks - Take pride in building and operating scalable, reliable, secure systems - 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 you had: - Built out data infrastructure from, or nearly from, scratch at a fast-growing startup. - Experience building ML/DL infrastructure and/or data infrastructure that feeds into training large ML models Don’t meet every single requirement? We still encourage you to apply. If you’re excited about our mission and eager to