Foundation Model Data Engineer

Sciforium

. Own the end-to-end creation of pre-training datasets for LLMs .

Posted 9/16/2026full-timeSan Francisco • California • United StatesMid-LevelSenior💰 $155,000 - $210,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and managing datasets for large language models and vision models, with a strong focus on data processing, statistical analysis, and pipeline design. Proficient in high-performance Python programming and data-at-scale frameworks to optimize model performance and address multimodal challenges.

Highest-signal resume keywords

Expert-Level Python SkillsData Processing Pipelines DesignExperience With Petabyte-Scale DatasetsMastery Of Data-At-Scale FrameworksBuilding Datasets For RLHF And DPO

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data CleaningFuzzy DeduplicationSignal ExtractionStatistical AnalysisSynthetic-Data GenerationHigh-Performance CodingEfficient Memory ManagementMultithreadingMultiprocessingDataset Management

Tools & Technologies

SparkRayWebDatasetParquet

Industry Keywords

Data ScienceMachine LearningLarge Language ModelsVision ModelsHuman-Labeling Workflows

Tech Stack

Tools & technologies

PythonRaySpark

About the role

Key responsibilities & impact

Own the end-to-end creation of pre-training datasets for LLMs
Define the mix of web data, code, books, and technical papers to optimize downstream model performance
Design and implement pipelines for data cleaning, exact and fuzzy deduplication, and signal extraction from petabytes of raw unstructured data
Lead development of post-training datasets, including SFT instructions, multi-turn dialogues, and RLHF/DPO preference modeling data
Drive acquisition and processing of vision and video data
Address multimodal alignment, video compression, and temporal data consistency
Develop high-throughput Python data-processing scripts using multiprocessing and multithreading
Conduct statistical analysis of training corpora to identify biases, knowledge gaps, and quality regressions
Design synthetic-data generation pipelines to augment gaps in natural datasets

Requirements

What you’ll need

5+ years of industry experience in Data Science or Machine Learning
Proven track record of building and managing datasets for foundation models
Expert-level Python skills
High-performance coding with multiprocessing and multithreading
Efficient memory management for large-scale data tasks
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 such as Common Crawl and specialized domain data
Hands-on experience building datasets for RLHF, DPO, and multi-turn instruction following
Experience managing human-labeling workflows and quality gold-sets
Mastery of data-at-scale frameworks such as Spark or Ray
Experience with high-performance data-loading formats such as WebDataset or Parquet
Must be legally authorized to work in the United States
A Master’s or PhD in a quantitative field focused on data-centric AI or information retrieval is a nice-to-have, not required

Benefits

Comp & perks

Medical, dental, and vision insurance
401k plan
Daily lunch, snacks, and beverages
Flexible time off
Competitive salary
Equity