Research Engineer

datologyai; San Mateo; On-site; PHP 11,314,000 - 18,856,000; Full time

Benefits offered by this job

100% health benefits

401(k) plan with 4% match

Unlimited PTO

Paid Parental Leave

Wellness stipend

Learning & development stipend

Daily lunches in office

Relocation assistance

Job summary

DatologyAI is hiring a Research Engineer to advance cutting-edge data curation and translate research into our core product pipeline. You will collaborate with researchers and engineers to scale pipelines for massive datasets and ensure production-ready systems.

The role emphasizes Python, PyTorch, and distributed compute, with opportunities to work on GPU clusters in a cloud environment. Competitive compensation and equity are offered.

Qualifications

• 3+ years building ML systems, data infrastructure, or large-scale distributed applications.
• Fluency in Python and hands-on experience with PyTorch.
• Experience with distributed data processing and/or distributed training (Spark, Ray, Dask, Snowflake).
• Comfort operating large-scale compute, including GPU clusters and cloud infrastructure.

Responsibilities

• Build and scale data processing and curation pipelines over large language, vision, and multimodal datasets.
• Design experimentation infrastructure to enable rapid, scalable research iterations.
• Harden research results into production-grade data systems used by customers.
• Profile and optimize large-scale training and data workloads for performance and cost.

Skills

Python fluency

PyTorch experience

Distributed systems

Software engineering fundamentals

Tools

PyTorch

Spark

Ray

Dask

Snowflake

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) and pretraining with domain-specific data (The Finetuner’s 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

As a Research Engineer, you will play a crucial role in conducting and enabling cutting-\u2011edge research and translating it into our core product pipeline. You will work closely with other members of the technical staff to develop and improve state-of-the-\u2011art data curation strategies. Your technical skills will accelerate our research and ensure that our product remains at the forefront of innovation.

What You'll Work On

• You'll build and scale the data processing and curation pipelines that operate over massive language, vision, and multimodal datasets, and make them fast, reliable, and cheap to run.

• You'll design the experimentation infrastructure that lets scientists iterate quickly at scale, turning a good idea into a running experiment in hours.

• Our work is guided by concrete customer needs and product outcomes. You'll take research results and harden them into production-\u201grade systems that customers depend on.

• You'll profile and optimize large-scale training and data workloads, because at frontier scale, performance and cost are research constraints.

About You

• 3+ years building ML systems, data infrastructure, or large-\u2016scale distributed applications.

• Strong software engineering fundamentals, fluency in Python, and hands-\u2011on experience with PyTorch.

• Experience with distributed data processing and/or distributed training, using tools such as Spark, Ray, Dask, or Snowflake.

• Comfort operating large-\u2016scale compute, including GPU clusters and cloud infrastructure.

• Enough machine learning depth to collaborate substantively with researchers, not just implement their specs.

• A demonstrated track record of shipping systems that others rely on, whether through production infrastructure, open-\u2011source tools, or other 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-\u2011related 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.