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.

Role Overview

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.

Key Responsibilities

• Foundation Dataset Strategy: 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.

• Petabyte-Scale Curation: Design and implement sophisticated pipelines for data cleaning, exact/fuzzy deduplication, and high-quality signal extraction from petabytes of raw, unstructured data.

• Post-Training & Alignment Data: Lead the 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 the acquisition and processing of vision and video data, navigating the complexities of multimodal alignment, video compression, and temporal data consistency.

• High-Performance Engineering: Develop high-throughput data processing scripts using Python, leveraging multiprocessing and multithreading to handle massive-scale ingestion and transformation without bottlenecks.

• Data Profiling & Analysis: 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.

• Synthetic Data Generation: (Added Value) Design pipelines to generate high-reasoning synthetic data to augment gaps in natural datasets, utilizing existing models for data labeling and refinement.