About this role
Sciforium is seeking a Foundation Model Data Engineer in San Francisco, CA to direct the creation and curation of large-scale datasets for advanced AI models. This full-time position suits an experienced data scientist or machine learning professional specializing in petabyte-scale data pipelines and model training infrastructure.
Role Overview
Sciforium develops multimodal artificial intelligence models and high-efficiency serving infrastructure. The organization requires a technical professional to manage the complete data lifecycle, from initial web crawling through human-alignment datasets. This contributor will build data taxonomies, filtering criteria, and post-training pipelines that influence model safety, reasoning, and multimodal capabilities.
Key Responsibilities
Requirements
Nice to Have
Skills and Tools
Salary and Benefits
About the Company
Sciforium functions as an artificial intelligence infrastructure enterprise focused on next-generation multimodal models and proprietary serving platforms. The company receives multi-million-dollar funding and direct sponsorship from AMD, featuring hands-on engineering collaboration to build the full technology stack for frontier models and real-time software.
How to Apply
Interested candidates should submit their application through the official Sciforium careers page.
Skills and tech stack
Questions candidates ask
What is the salary range for the Foundation Model Data Engineer role?
The salary ranges from $155,000 to $210,000, and the position includes equity offerings.
Where is the job located?
The position is located in San Francisco, CA.
How many years of experience are required?
Candidates must have 5+ years of industry experience in Data Science or Machine Learning.
What benefits are provided with this position?
Benefits include medical, dental, and vision insurance, a 401k plan, flexible time off, daily lunch, snacks, beverages, and equity.
Is a specific educational degree mandatory?
A Master's or PhD in a quantitative field is listed under nice-to-haves rather than a strict requirement.