Research Scientist, Data
P
PikaGenerative AI
US remoteFull-TimeSenior
SalaryBase Salary $185K – $400K • 0.1% – 0.3%
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Job Details
- Experience
- 5+ years of experience
- Required Skills
- AWSPythonSQLETLGCPAzureData engineeringPySpark
Requirements
- Have 5+ years of experience building and scaling data pipelines for machine learning applications.
- Bring staff- or lead-engineer-level experience, ideally in research or model-training environments.
- Have a strong background in data engineering and ML data curation for LLMs, VLMs, or other large-scale multimodal models.
- Have expertise in distributed data systems such as Spark, Hadoop, or Ray, or similar systems.
- Have experience with efficient large-dataset processing and ETL workflows.
- Be able to build robust, scalable, production-grade data infrastructure for ML pipelines.
- Have experience developing tools for data labeling, filtering, deduplication, quality assurance, and dataset management.
- Have strong programming skills in Python, SQL, PySpark, or similar languages.
- Be familiar with cloud data platforms such as AWS, GCP, or Azure.
- Know privacy, compliance, ethics, and best practices in data collection and management.
- Bring cross-functional collaboration, problem-solving, and communication skills.
Responsibilities
- Architect and implement large-scale data pipelines for model training and research workflows across text, image, audio, and video datasets.
- Partner with research and engineering teams to curate, clean, and manage datasets for multimodal model pre-training and mid-training.
- Develop tools and strategies for scalable data ingestion, labeling, filtering, augmentation, and storage.
- Manage data quality, reliability, privacy, ethical considerations, and compliance throughout the data lifecycle.
- Optimize data processing, transformation, and delivery for large-scale distributed training pipelines.
- Prototype and productionize methods for dataset creation, management, and continuous improvement.
- Integrate research-driven data advancements into production-ready systems.
- Apply emerging data engineering and ML data management practices to the systems.
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