---
title: 'Research Scientist, Post-Training at DatologyAI'
canonical: 'https://feeny.ai/job/research-scientist-post-training-datologyai-san-mateo-b9tsptwy59j9'
type: 'job'
last_seen: '2026-10-17'
---

# Research Scientist, Post-Training at DatologyAI

- **Company:** DatologyAI
- **Location:** San Mateo, CA
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-10-07
- **Last confirmed live:** 2026-10-17
- **Apply:** https://jobs.ashbyhq.com/datologyai/55b90643-0bae-4074-8ee3-e05464757bd0

## Job description

## 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](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. 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

We’re looking for a Research Scientist to lead work on post-training data curation for foundation models. You’ll design and implement algorithms to generate and improve instruction, preference, and other post-training datasets. You’ll also help bridge the gap between pre-training and post-training by exploring how to jointly optimize data across stages. This role requires strong scientific judgment, fluency with the deep learning literature, and a drive to turn research ideas into real-world impact. You’ll work autonomously, collaborate closely with engineers and product teams, and shape the future of data curation at DatologyAI.

## What You'll Work On

- Post-training data curation. You’ll conduct research on how to algorithmically curate post-training data—e.g., how to generate and refine preference and instruction-following data, how to curate capability- and domain-specific data, and make post-training more effective, controllable, and generalizable.
- Unifying pre-training and post-training data curation. Pushing the bounds on model capabilities requires unifying post-training and pre-training data curation. You will pursue research on end-to-end data curation: how to curate pre-training data to improve the post-trainability of models and how to jointly optimize pre- and post-training data curation, all in service of maximizing the final performance of post-trained 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.

## How You'll Work

- 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.
- Science is more than just experiments. We expect our Research Scientists to collaborate closely with engineers, talk to customers, and shape the product vision.

## About You

- 3+ years of deep learning research experience
- Experience with post-training large vision, language, and multimodal models
- Post-training algorithm development, data curation, and/or synthetic data methods for:
- Preference-based tuning (e.g. DPO, RLVR, RRHF)
- Alternative supervision & self-supervision techniques such as self-training and chain-of-thought distillation
- SFT (e.g. instruction tuning and demonstration fine-tuning)
- Post-training tooling development and engineering experience
- 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.

## Nice to 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 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.

## 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.

## About DatologyAI

## Company Overview
- **One-liner**: DatologyAI provides an automated platform that curates training data for deep learning models, helping companies train better AI models faster and more cost-effectively.
- **Entity Type**: Private (Series A)
- **Headquarters**: Redwood City, California, United States
- **Founded**: 2023
- **Founders**: Ari Morcos (CEO), Bogdan Gaza (CTO), Matthew Leavitt

## Core Business
- **Primary industry/industries**: Artificial intelligence, Machine learning infrastructure, Data curation
- **Target customers**: B2B — Enterprises and AI teams training large-scale deep learning models across verticals
- **Mission or purpose statement**: "Democratize AI data curation, allowing every company to easily train its own custom model on the right data without needing to invest massive resources." [datologyai.com](https://www.datologyai.com/about)

## Products & Services
- **DatologyAI Platform**: A fully automated, scalable data curation platform that identifies redundant, noisy, or harmful data points in training datasets. It integrates with existing infrastructure (from blob storage to dataloader), is modality-agnostic (works for text, images, etc.), and requires no labels. The goal is to optimize training efficiency, maximize model performance, and reduce compute costs. [datologyai.com](https://www.datologyai.com/about)

## Market Standing
- **Valuation/Market Cap**: Not publicly available
- **Key Metric**: Total funding of $57.65M [cbinsights.com](https://www.cbinsights.com/company/datologyai)
- **Notable Investors/Partners**: M12 (Microsoft’s venture fund), Amplify Partners, Amazon Alexa Fund [datologyai.com](https://www.datologyai.com/about)
- **Growth Signals**: Founded in 2023 and already raised $57.65M (including a $46M Series A in 2024); backed by top-tier investors; founded by researchers with backgrounds at DeepMind, Meta AI (FAIR), and MosaicML (acquired by Databricks); cutting-edge research (80% novel and unpublished) directly powers the product.

## Competitive Advantages
- **Research-driven product**: The platform is built on frontier research in data curation for deep learning, with the team publishing novel techniques at top conferences (NeurIPS, ICLR).
- **Modality-agnostic & label-free**: Works across data types and doesn’t require labeled data, making it applicable to a wide range of use cases.
- **Deep technical team**: Founders bring expertise from leading AI labs (DeepMind, Meta AI, MosaicML) and large-scale infrastructure experience (Amazon, Twitter).
- **Customer-obsessed and scalable**: Designed to be easy to implement and generalize across models, directly reducing compute costs for customers.

## Strategic Focus
- **Product & research**: Continue to push the boundaries of data curation research and deploy it directly into the product to improve model efficiency and capability.
- **Customer success**: Deeply focus on customer problems and goals to drive adoption and success.
- **Scaling the team**: Actively hiring across engineering, research, sales, and product roles to grow the organization.

## Why Work Here
- **Impactful mission**: Solve the "biggest problem in AI model training" — data quality — with research that has real-world impact.
- **World-class team**: Work alongside researchers and engineers from DeepMind, Meta AI, and MosaicML.
- **Culture of research**: Engineering and research are deeply intertwined; ideas are deployed into production, not just published.
- **In-office culture**: HQ in Redwood City, CA, with a strong in-office expectation ("In-Office" listed for all roles on Built In). [builtin.com](https://builtin.com/company/datologyai)
- **Benefits**: 100% covered health benefits (medical, vision, dental), 401(k) with 4% match, unlimited PTO, $2,000 annual wellness stipend, $1,000 annual L&D stipend, daily lunch/snacks, dinner for late work, relocation assistance. [datologyai.com](https://www.datologyai.com/careers)
- **Fast-paced, high-growth environment**: A chance to grow fast and make a significant impact at a well-funded startup.

## Sources
1. [DatologyAI About Page](https://www.datologyai.com/about)
2. [DatologyAI Careers Page](https://www.datologyai.com/careers)
3. [Built In Company Profile](https://builtin.com/company/datologyai)
4. [CB Insights Company Profile](https://www.cbinsights.com/company/datologyai)
5. [LinkedIn Company Page](https://www.linkedin.com/company/datologyai)

## Other roles at DatologyAI

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- [Software Engineer, Infrastructure](https://feeny.ai/job/software-engineer-infrastructure-datologyai-san-mateo-27yxqv5jp2gv) — San Mateo, CA
- [AI Developer Experience & Media Lead](https://feeny.ai/job/ai-developer-experience-media-lead-datologyai-san-mateo-pf7jfn1t6ad9) — San Mateo, CA
- [Research Engineer](https://feeny.ai/job/research-engineer-datologyai-san-mateo-5cps4zyfezck) — San Mateo, CA
- [Forward Deployed AI Engineer (Post-Sales)](https://feeny.ai/job/forward-deployed-ai-engineer-post-sales-datologyai-san-mateo-exyc2z5wjfcx) — San Mateo, CA
- [Research Scientist](https://feeny.ai/job/research-scientist-datologyai-san-mateo-01jmy0y7mzym) — San Mateo, CA
- [Product Manager](https://feeny.ai/job/product-manager-datologyai-san-mateo-f6ef705z2p47) — San Mateo, CA
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