7mo ago

Member of Technical Staff, Data Infrastructure

Bay Areafull-timelead

OtherMember Of Technical Staff

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Quick Summary

Key Responsibilities

distributed compute, data orchestration, and storage across modalities. Develop high-throughput systems for data ingestion, processing, and transformation — including training data catalogs,

Technical Tools

OtherMember Of Technical Staff

The 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).

What We Offer

~1 min read

The annual base salary range for this role is $200,000 – $350,000 USD. Final compensation is determined based on experience, skills, and qualifications. Equity and benefits are included in the total package.