# Foundation Model Data Engineer at Sciforium

- Company: Sciforium
- What the company does: Sciforium builds the next generation of AI models with unprecedented efficiency, privacy, and versatility. Backed by SignalFire.
- Company website: https://sciforium.com
- Type: Startups
- Level: Mid level
- Location: San Francisco, CA
- Work setup: On-site
- Pay: $155K to $210K base salary per year (USD)
- Posted: 2026-09-28
- Apply by: 2026-11-12
- Apply: https://jobs.ashbyhq.com/Sciforium/8c52730f-a9ae-412f-914f-3f700622c34b
- Page: https://www.1752.vc/careers/jobs/sciforium-foundation-model-data-engineer/

## About the role

Sciforium is seeking a highly technical and visionary Foundation Model Data Engineer to lead the strategy, creation, and curation of the massive datasets that power our foundation models. We believe that in the era of LLMs, data is the primary competitive advantage. In this role, you will own the end-to-end data lifecycle—from raw web-scale crawling to the fine-grained human-alignment datasets that define model behavior.

## What they're looking for

- 5+ years of industry experience in Data Science or Machine Learning, with a proven track record of building and managing datasets for foundation models
- Deep Proficiency in Python: Expert-level skills with a focus on high-performance code, including multiprocessing, multithreading , and efficient memory management for large-scale data tasks
- Petabyte-Scale Experience: Demonstrated experience working with petabyte-scale datasets that have been directly used to train production-grade LLMs or Large Vision Models
- Dataset Reconstruction: Experience building massive LLM training sets from scratch , including raw web crawls (e.g., Common Crawl) and specialized domain data
- Post-Training Expertise: Hands-on experience building datasets for RLHF, DPO, and multi-turn instruction following , including the management of human-labeling workflows and quality gold-sets
- Data Tooling: Mastery of data-at-scale frameworks such as Spark, Ray, or high-performance data-loading formats (e.g., WebDataset, Parquet)

Tags: Engineering