Member of Technical Staff, Data Infrastructure at INCEPTION ARTIFICIAL INTELLIGENCE L.L.C - O.P.C

San Francisco, California, United States -

Full Time

Start Date

Immediate

Expiry Date

08 Jun, 26

Salary

0.0

Posted On

10 Mar, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Infrastructure, Distributed Training Pipelines, Data Catalogs, LLM Research, Data Orchestration, Data Ingestion, Data Processing, Web Crawling, Data Storage, Data Retrieval, Data Versioning, Python, Apache Spark, Beam, Airflow, PyTorch

Industry

technology;Information and Internet

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).

Responsibilities

The role involves designing, building, and operating scalable, fault-tolerant infrastructure for Large Language Model research, covering distributed compute, data orchestration, and storage across modalities. Responsibilities also include developing high-throughput systems for data ingestion, processing, transformation, and building systems for web crawling and real-time data processing.