Foundation Model Data Engineer

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.

This position is ideal for a scientist who views data as a high-scale engineering challenge and an analytical puzzle. You will not just "provide" data; you will design the taxonomies, filtering heuristics, and post-training pipelines that ensure our models are world-class in reasoning, safety, and multimodal understanding.

About Sciforium

Sciforium is an AI infrastructure company developing next-generation multimodal AI models and a proprietary, high-efficiency serving platform. Backed by multi-million-dollar funding and direct sponsorship from AMD with hands-on support from AMD engineers the team is scaling rapidly to build the full stack powering frontier AI models and real-time applications.

Responsibilities

• Lead the development of high-quality post-training datasets, including Supervised Fine-Tuning (SFT) instructions, multi-turn dialogues, and preference modeling data (RLHF/DPO).
• Drive the acquisition and processing of vision and video data, navigating the complexities of multimodal alignment, video compression, and temporal data consistency.
• Develop high-throughput data processing scripts using Python, leveraging multiprocessing and multithreading to handle massive-scale ingestion and transformation without bottlenecks.
• Conduct deep-dive statistical analysis on training corpora to identify biases, gaps in knowledge, and quality regressions, ensuring the "diet" of the model is mathematically balanced.
• (Added Value) Design pipelines to generate high-reasoning synthetic data to augment gaps in natural datasets, utilizing existing models for data labeling and refinement.

Requirements

• Own the end-to-end creation of pre-training datasets for LLMs. This includes defining the mix of web data, code, books, and technical papers to optimize for downstream model performance.
• Design and implement sophisticated pipelines for data cleaning, exact/fuzzy deduplication, and high-quality signal extraction from petabytes of raw, unstructured data.
• 5+ years of industry experience in Data Science or Machine Learning, with a proven track record of building and managing datasets for foundation models.
• Expert-level skills with a focus on high-performance code, including multiprocessing, multithreading, and efficient memory management for large-scale data tasks.
• Demonstrated experience working with

petabyte-scale datasets

• that have been directly used to train production-grade LLMs or Large Vision Models.
• Experience building massive LLM training sets from scratch, including raw web crawls (e.g., Common Crawl) and specialized domain data.

Hands-on experience building datasets for

• RLHF, DPO, and multi-turn instruction following, including the management of human-labeling workflows and quality gold-sets.
• Mastery of data-at-scale frameworks such as Spark, Ray, or high-performance data-loading formats (e.g., WebDataset, Parquet).
• Familiarity with large-scale crawling of multimodal data and the associated challenges of video processing, codecs, and compression.

Preferred

• Experience building large-scale image or video datasets from scratch (e.g., LAION-style pipelines).
• Experience in designing complex labeling schemas for reasoning, coding, and mathematical benchmarks.
• A Master’s or PhD in a quantitative field with a focus on data-centric AI or information retrieval.

Benefits

• 401k plan
• Daily lunch, snacks, and beverages

Equal opportunity

Sciforium is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.