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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, the company is scaling rapidly to build the full stack powering frontier AI models and real-time applications.
You will lead the strategy, creation, and curation of massive datasets that power foundation models. This role owns the end-to-end data lifecycle—from raw web-scale crawling to fine-grained human-alignment datasets that define model behavior. You will design taxonomies, filtering heuristics, and post-training pipelines to ensure models excel in reasoning, safety, and multimodal understanding.
Key responsibilities include:
- Foundation Dataset Strategy: Own end-to-end creation of pre-training datasets for LLMs, defining the optimal mix of web data, code, books, and technical papers.
- Petabyte-Scale Curation: Design and implement sophisticated pipelines for data cleaning, deduplication, and high-quality signal extraction from petabytes of raw, unstructured data.
- Post-Training & Alignment Data: Lead development of high-quality post-training datasets, including Supervised Fine-Tuning (SFT) instructions, multi-turn dialogues, and preference modeling data (RLHF/DPO).
- Multimodal Expansion: Drive acquisition and processing of vision and video data, navigating multimodal alignment, video compression, and temporal data consistency.
- High-Performance Engineering: Develop high-throughput data processing scripts using Python, leveraging multiprocessing and multithreading for massive-scale ingestion and transformation.
- Data Profiling & Analysis: Conduct deep-dive statistical analysis on training corpora to identify biases, knowledge gaps, and quality regressions.
- Synthetic Data Generation: Design pipelines to generate high-reasoning synthetic data to augment gaps in natural datasets.
REQUIREMENTS:
- 5+ years of industry experience in Data Science or Machine Learning with proven track record building and managing datasets for foundation models
- Expert-level Python proficiency with focus on high-performance code, multiprocessing, multithreading, and efficient memory management for large-scale data tasks
- Demonstrated experience working with petabyte-scale datasets 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 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)
Nice-to-haves: Computer Vision curation experience, multimodal crawling familiarity, taxonomy design expertise, Master's or PhD in a quantitative field with focus on data-centric AI or information retrieval.
