Member of Technical Staff, Data
Bay Area
- Pay
- Salary not listed in the saved posting
- Work setup
- Unconfirmed
- Employment
- Unconfirmed
What you’ll work on
Full postingDevelop data mixes for training LLMs, including by leveraging open-source datasets, synthetically generated data, and curated human feedback.
Design and implement data pipelines for processing petabyte-scale datasets.
Build systems for web crawling, data ingestion, and real-time data processing to support model training.
From the employer’s posting
Key Responsibilities Develop data mixes for training LLMs, including by leveraging open-source datasets, synthetically generated data, and curated human feedback. Design and implement data pipelines for processing petabyte-scale datasets.
Develop data mixes for training LLMs, including by leveraging open-source datasets, synthetically generated data, and curated human feedback. Design and implement data pipelines for processing petabyte-scale datasets. Build systems for web crawling, data ingestion, and real-time data processing to support model training.
Design and implement data pipelines for processing petabyte-scale datasets. Build systems for web crawling, data ingestion, and real-time data processing to support model training. Develop tools and frameworks for efficient data storage, retrieval, and versioning across distributed systems.
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 and scientists to shape how we collect, process, and curate the datasets that power our models. You'll combine engineering expertise with research insight to build scalable data pipelines, develop synthetic data generation techniques, and ensure our models are trained on high-quality, diverse data.
Key Responsibilities
- Develop data mixes for training LLMs, including by leveraging open-source datasets, synthetically generated data, and curated human feedback.
- Design and implement data pipelines for processing petabyte-scale datasets.
- Build systems for web crawling, data ingestion, and real-time data processing to support model training.
- Develop tools and frameworks for efficient data storage, retrieval, and versioning across distributed systems.
- Create evaluation frameworks to measure data diversity, quality, and representativeness.
- 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.
Already applied? Track this application
Source & posting history
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Bay Area
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.
- Status in our records
- Active
- First seen by us
- Jun 2, 2026
- Recorded sightings
- 66
- 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.
Report an errorSee how this role fits your experience
Add your resume to compare the role’s scope, tools and requirements with your experience.