Datology AI
Research Engineer
About this role
DatologyAI is seeking a Research Engineer to advance cutting-edge data curation technology that optimizes training data for machine learning models. You'll conduct research and translate it into product features, working with a team backed by major investors and AI leaders to solve critical challenges in model training efficiency.
What you'll do
- Conduct research on data curation strategies and optimization techniques
- Translate research findings into scalable product implementations
- Develop and improve state-of-the-art data curation methodologies
- Collaborate with technical staff on machine learning systems and foundation models
- Contribute to research community through publications and open-source projects
- Work across the research-to-product pipeline to validate and deploy solutions
What they're looking for
- Machine learning systems and distributed training
- Foundation model architecture and training
- ML performance optimization
- Software engineering and empirical research
- Data curation and quality assessment
- Large-scale model optimization
- Research publication and presentation
- Independent and collaborative problem-solving
Benefits
- Fully covered health insurance (medical, vision, dental)
- 401(k) with 4% company match
- Unlimited PTO and 12 weeks paid parental leave
- Annual wellness ($2,000) and learning stipends ($1,000)
- Office lunches and snacks provided
- Relocation assistance available
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Datology AI
DatologyAI builds a data curation platform that optimizes AI training datasets to reduce costs and improve model performance. The company is hiring cloud infrastructure engineers, data platform engineers, full-stack product engineers, and solutions engineers to scale its multi-cloud infrastructure and customer-facing tools.
View all jobs at Datology AILikely interview questions
- Can you walk us through a recent research project where you optimized machine learning model performance? What metrics did you track and how did you measure success?
- Describe your experience with distributed training of large foundation models. What challenges have you encountered and how did you address them?