Member of Technical Staff, Data Infrastructure
Bay Area
- Pay
- Salary not listed in the saved posting
- Work setup
- Unconfirmed
- Employment
- Unconfirmed
What you’ll work on
Full postingYou'll work directly with researchers to accelerate experiments, develop new datasets, improve infrastructure efficiency, and enable key insights across our data assets.
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.
From the employer’s posting
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
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.
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.
What you’ll bring
All qualificationsCore 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 experience
- 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.
Qualification wording
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.
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.
Tools in this posting
- Python
- SQL
- BigQuery
- NoSQL
- S3
- Spark
- PyTorch
- TensorFlow
- Airflow
Source — Tool mentions in context
- 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.
- 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
- Knowledge of data privacy regulations and ethical AI practices. - Experience with distributed computing and large-scale data storage systems (HDFS, S3, BigQuery).
- 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.
Job description
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).
Your next step
- Have your CV and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
- Ask the employer about the salary range before committing time to the process.
Complete your application on jobs.gem.com. The employer’s form will show what is required.
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Source & posting history
Source notes
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- Pay
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- Location & working pattern
Bay Area
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- Work authorization
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- Status in our records
- Active
- First seen by us
- Jun 2, 2026
- Recorded sightings
- 64
- Last seen by us
- Oct 2, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
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