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.
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.
Qualifications
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 multi-modal 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.
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