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.
Following our $11.65M Seed round last September, we've raised a $46M Series A led by Felicis Ventures. Our investors include Radical Ventures, Amplify Partners, Microsoft, Amazon, and notable angels like Jeff Dean, Geoff Hinton, Yann LeCun and Elad Gil. With over $57.5M in total funding, we're rapidly scaling our team and computing resources to revolutionize data curation across modalities.
Join us in pushing the boundaries of what's possible in AI! Learn more about the company here.
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.
This role is based in Redwood City, CA. We are in person 4 days a week and offer relocation assistance to new employees. We provide visa sponsorship for candidates selected for this role.
We would like to hire researchers with practical experience and/or publications related to 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.
At DatologyAI, we are dedicated to rewarding talent with highly competitive salary and significant equity. The salary for this position ranges from $150,000 to $250,000.
The candidate's starting pay will be determined based on job-related skills, experience, qualifications, and interview performance.
We also offer a comprehensive benefits package to support our employees' well-being and professional growth:
100% covered health benefits (medical, vision, and dental).
401(k) plan with a generous 4% company match.
Unlimited paid time off (PTO) policy.
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.
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DatologyAI is on the lookout for a talented Research Scientist to join our bustling team in Redwood City! If you’re passionate about transforming how training data can enhance deep learning models, this role is perfect for you. At DatologyAI, we take pride in turning extensive research into practical tools for enterprise customers, helping them identify the most effective data for training. Your main responsibilities will include sifting through a vast array of literature to unearth practical improvements while meeting real-world customer needs. We see science as a collaborative effort, so you'll actively work alongside engineers, engage with customers, and help shape our product vision. We value the unique insights you can bring—your autonomy to tackle problems your way is essential to our mission. With a supportive environment, ample resources, and a focus on meaningful research, you'll see your ideas come to fruition in innovative ways. We’re excited to elevate the AI landscape and would love for you to be part of our journey. Join us at DatologyAI and make an impact in the industry with your expertise in data research, data curation, and deep learning!
datologyai builds tools to automatically select the best data on which to train deep learning models. our tools leverage cutting-edge research—much of which we perform ourselves—to identify redundant, noisy, or otherwise harmful data points. the a...
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