Datology AI

Research Scientist, Post-Training

Redwood City

Sponsorship not specified$180k-$300kDetected 371 days ago

AlgorithmsSnowflakeMachine LearningDeep LearningPyTorchSparkData EngineeringResearchCommunicationCollaborationAdaptability

About the role

Unifying pre-training and post-training data curation.
Pushing the bounds on model capabilities requires unifying post-training and pre-training data curation.
Transform messy literature into practical improvements.

Responsibilities

You'll design and implement algorithms to generate and improve instruction, preference, and other post-training datasets.
You'll work autonomously, collaborate closely with engineers and product teams, and shape the future of data curation at DatologyAI.
You will use your skills as a scientist to source, vet, implement, and improve promising ideas from the literature and of your own creation.

Requirements

Experience with data management and distributed data processing solutions (e.g. Spark, Snowflake, etc.)

Skills

Demonstrated track record of success in deep learning research, whether papers, tools, or other research 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.
401(k) plan with a generous 4% company match.
Annual $2,000 wellness stipend.
Annual $1,000 learning and development stipend.
Daily lunches and snacks are provided in our office!

Benefits

100% covered health benefits (medical, vision, and dental).
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.
We expect our Research Scientists to collaborate closely with engineers, talk to customers, and shape the product vision.

Company info

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.
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.
This role is based in Redwood City, CA.
We are in office 4 days a week.
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.
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.
At DatologyAI, we understand that conference reviewers and academic benchmarks don't always incentivize the most impactful research.

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