Foundation Model Data Engineer

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.

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. In the era of LLMs, data is the primary competitive advantage. 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—designing taxonomies, filtering heuristics, and post‑training pipelines to ensure world‑class reasoning, safety, and multimodal understanding.

Key Responsibilities

• Foundation Dataset Strategy: Define and create pre‑training datasets for LLMs, selecting the optimal mix of web data, code, books, and technical papers.
• Petabyte‑Scale Curation: Design and implement pipelines for data cleaning, exact/fuzzy 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: Acquire and process vision and video data, handling multimodal alignment, video compression, and temporal consistency.
• High‑Performance Engineering: Build high‑throughput data processing scripts in Python, leveraging multiprocessing and multithreading for massive‑scale ingestion and transformation.
• Data Profiling & Analysis: Perform statistical analysis on training corpora to identify biases, knowledge gaps, and quality regressions, ensuring a mathematically balanced model “diet”.
• Synthetic Data Generation (Added Value): Design pipelines to generate high‑reasoning synthetic data to augment natural datasets, using existing models for labeling and refinement.

Must-Haves

• 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, with expert‑level skills in high‑performance code, multiprocessing, multithreading, and efficient memory management for large‑scale data tasks.
• Petabyte‑scale experience: demonstrated work with petabyte‑scale datasets used to train production‑grade LLMs or large vision models.
• Dataset reconstruction: experience building massive LLM training sets from raw web crawls (e.g., Common Crawl) and specialized domain data.
• Post‑training expertise: hands‑on experience building RLHF, DPO, and multi‑turn instruction datasets, managing human‑labeling workflows and quality gold‑sets.
• Data tooling mastery: proficiency with data‑at‑scale frameworks such as Spark, Ray, or high‑performance data‑loading formats (e.g., WebDataset, Parquet).

Nice-to-Haves

• Computer Vision (CV) curation: experience building large‑scale image or video datasets from scratch (e.g., LAION‑style pipelines).
• Multimodal crawling: familiarity with large‑scale crawling of multimodal data and challenges of video processing, codecs, and compression.
• Taxonomy design: experience designing complex labeling schemas for reasoning, coding, and mathematical benchmarks.
• Research background: a Master’s or PhD in a quantitative field focused on data‑centric AI or information retrieval.

Benefits

• Medical, dental, and vision insurance
• 401(k) plan
• Daily lunch, snacks, and beverages
• Flexible time off
• Competitive salary and equity

Pay

Compensation Range: $155K – $210K