Member of Technical Staff, Data Infrastructure

Inception; San Francisco (CA); On-site; USD 140,000 - 190,000; Full time

Job summary

Inception seeks experienced engineers to architect and scale the core infrastructure behind distributed training pipelines and petabyte-scale data catalogs. You will work directly with researchers to accelerate experiments, develop new datasets, improve infrastructure efficiency, and enable key insights across our data assets.

You will design, build, and operate scalable, fault-tolerant infrastructure for LLM research, including distributed compute, data orchestration, and storage; develop

Qualifications

• > BS/MS/PhD in Computer Science, Machine Learning, or a related field (or equivalent experience).
• > 3+ years of experience building data processing pipelines at scale, particularly with AI/ML applications.
• > Proficiency in Python and data processing frameworks (Spark, Beam, Airflow).
• > Familiarity with synthetic data generation and data augmentation.
• > Familiarity with web scraping, crawling tech, and Common Crawl datasets.
• > Knowledge of ML fundamentals and frameworks (PyTorch, TensorFlow).
• > Experience with SQL and NoSQL databases.

Responsibilities

• Design, build, and operate scalable, fault-tolerant infrastructure for LLM research: distributed compute, data orchestration, and storage.
• Develop high-throughput systems for data ingestion, processing, and transformation — including training data catalogs, deduplication, quality checks, and search.
• Build systems for web crawling, data ingestion, and real-time data processing to support model training operations.
• Develop tools and frameworks for efficient data storage, retrieval, and versioning across distributed systems.
• Ensure data collection adheres to privacy regulations.

Skills

Python

Spark

Airflow

Beam

PyTorch

TensorFlow

SQL

NoSQL

Web scraping

Common Crawl

Synthetic data

Education

CS/ML degree

Job description

Role

We seek experienced engineers to architect and scale the core infrastructure behind distributed training pipelines and petabyte-scale data catalogs. You\'ll work directly with researchers to accelerate experiments, develop new datasets, improve infrastructure efficiency, and enable key insights across our data assets.

Key Responsibilities

• Design, build, and operate scalable, fault-tolerant infrastructure for LLM research: distributed compute, data orchestration, and storage across modalities.
• Develop high-throughput systems for data ingestion, processing, and transformation — including training data catalogs, deduplication, quality checks, and search.
• Build systems for web crawling, data ingestion, and real-time data processing to support model training operations.
• Develop tools and frameworks for efficient data storage, retrieval, and versioning across distributed systems.
• Ensure data collection adheres to privacy regulations.

Qualifications

• BS/MS/PhD in Computer Science, Machine Learning, or a related field (or equivalent experience).
• 3+ years of experience building data processing pipelines at scale, particularly with AI/ML applications.
• Strong proficiency in Python and experience with data processing frameworks (Apache Spark, Beam, Airflow).
• Familiarity with synthetic data generation techniques and data augmentation strategies.
• Familiarity with web scraping, crawling technologies, and Common Crawl datasets.
• Solid understanding of machine learning fundamentals and experience with ML frameworks (PyTorch, TensorFlow).
• Experience with SQL and NoSQL databases for managing structured and unstructured data.

Preferred Skills

• Experience with large language models and understanding of tokenization, embeddings, and model architectures.
• Experience managing human annotation workflows and quality control processes.
• Experience with vector databases and embedding-based retrieval systems.
• Knowledge of data privacy regulations and ethical AI practices.
• Experience with distributed computing and large-scale data storage systems (HDFS, S3, BigQuery).