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) and pretraining with domain-specific data (The Finetuner’s 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 San Mateo, CA. We are in office 4 days a week.
About the Role
As a Research Engineer, you will play a crucial role in conducting and enabling cutting-edge research and translating it into our core product pipeline. You will work closely with other members of the technical staff to develop and improve state-of-the-art data curation strategies. Your technical skills will accelerate our research and ensure that our product remains at the forefront of innovation.
What You'll Work On
You'll build and scale the data processing and curation pipelines that operate over massive language, vision, and multimodal datasets, and make them fast, reliable, and cheap to run.
You'll design the experimentation infrastructure that lets scientists iterate quickly at scale, turning a good idea into a running experiment in hours.
Our work is guided by concrete customer needs and product outcomes. You'll take research results and harden them into production-grade systems that customers depend on.
You'll profile and optimize large-scale training and data workloads, because at frontier scale, performance and cost are research constraints.
About You
3+ years building ML systems, data infrastructure, or large-scale distributed applications.
Strong software engineering fundamentals, fluency in Python, and hands-on experience with PyTorch.
Experience with distributed data processing and/or distributed training, using tools such as Spark, Ray, Dask, or Snowflake.
Comfort operating large-scale compute, including GPU clusters and cloud infrastructure.
Enough machine learning depth to collaborate substantively with researchers, not just implement their specs.
A demonstrated track record of shipping systems that others rely on, whether through production infrastructure, open-source tools, or other artifacts.
Compensation
At DatologyAI, we are dedicated to rewarding talent with competitive salary and meaningful equity. The salary for this position ranges from $180,000 to $300,000.
Starting pay is based on job-related skills, experience, qualifications, and interview performance.
Benefits
100% covered health benefits (medical, vision, and dental).
401(k) plan with a generous 4% company match.
Unlimited PTO policy
Paid Parental Leave of 12 weeks, plus 6 months of WFH flexibility.
Annual $2,000 wellness stipend.
Annual $1,000 learning and development stipend.
Daily lunches and snacks are provided in our office!
Relocation assistance for employees moving to the Bay Area.