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SalaryAbout Us
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.
About the Role
As a Research Scientist at DatologyAI, you will conduct research investigating how intervening on training data can improve the quality and shape the behavior of deep learning models.
Transform messy literature into practical improvements. The research literature is vast, rife with ambiguity, and constantly evolving. You will use your skills as a scientist to source, vet, implement, and improve promising ideas from the literature and of your own creation.
Conduct science driven by real-world needs. At DatologyAI, we understand that conference reviewers and academic benchmarks don’t always incentivize the most impactful research. Your research will be guided by concrete customer needs and product improvements.
Science is more than just experiments. We expect our Research Scientists to collaborate closely with engineers, talk to customers, and shape the product vision.
Nobody knows how to do your work better than you. We believe that scientists do their best work when they have the autonomy to pursue problems in the manner they prefer, and we will ensure that you are equipped with the context and resources you need to succeed.
About You
Ideal candidates will have experience with at least one of the following:
We would like to hire researchers with practical experience and/or publications related any of the following research topics
Data research
Data pruning/curation
Curriculum learning
Synthetic data generation
Dataset distillation
Effects of training data on model behavior
Embedding models
Semantic search
Efficient ML
We would like to hire researchers with practical experience and/or publications related to training large vision (especially video), language, and multimodal models.
Candidates should also have the following qualifications:
Strong understanding of the fundamentals of deep learning.
Sufficient software engineering + deep learning framework (PyTorch or a willingness to learn PyTorch) skills to conduct large-scale research experiments and build production prototypes.
Demonstrated track record of success in deep learning research, whether papers, tools, or other research artifacts.
We would love it if candidates have:
Experience with data management and distributed data processing solutions (e.g. Spark, Snowflake, etc.)
Experience building + shipping ML products
Candidates do not need a PhD or extensive publications. Some of the best researchers we’ve worked with have had no formal training in machine learning, and obtained all of their experience by working in industry and building products. We believe that adaptability, combined with exceptional communication and collaboration skills are the most important ingredients for successful research in a startup environment.
