Research Scientist, Data

Pika•Palo Alto, CA

1d•$185,000 - $400,000•Hybrid

About The Position

At Pika, we are pioneering the next generation of creative infrastructure built around real-time, multimodal generation and intelligent agentic platforms. We are looking for a staff or lead-level Research Engineer, Data to architect and scale data engineering systems supporting model training for our advanced multimodal foundation models. This pivotal role will strengthen our research teams by building, optimizing, and owning large-scale data pipelines and robust ML data curation, ensuring our foundation models have access to the highest quality and most diverse datasets. If you are passionate about powerful data infrastructure and innovative research-engineering, join us to make an impact for millions of creators.

Requirements

5+ years of experience building and scaling data pipelines for machine learning applications at staff or lead engineer level, ideally in research or model training environments
Strong background in data engineering and ML data curation for LLMs, VLMs, or other large-scale multimodal models
Expertise in distributed data systems (e.g., Spark, Hadoop, Ray, or similar) and efficient large dataset processing/ETL workflows
Proven ability to build robust, scalable, and production-grade data infrastructure for ML pipelines
Experience developing tools for data labeling, filtering, deduplication, quality assurance, and dataset management
Strong programming skills (Python, SQL, PySpark, or similar) and familiarity with cloud data platforms (AWS, GCP, Azure)
Knowledge of privacy, compliance, ethics, and best practices in data collection and management
Excellent cross-functional collaboration, problem-solving, and communication skills
Passion for enabling cutting-edge generative AI and creative technology through data excellence

Responsibilities

Take ownership of large-scale data pipeline architecture and implementation to support model training and research workflows for text, image, audio, and video datasets
Partner with research and engineering teams to curate, clean, and manage diverse, sensory-rich datasets for pre-training and mid-training of multimodal models
Develop strategies and tools for scalable data ingestion, labeling, filtering, augmentation, and storage
Ensure data quality, reliability, and compliance, including managing privacy and ethical considerations throughout the data lifecycle
Optimize data processing, transformation, and delivery for large-scale distributed training pipelines
Prototype and productionize new methods for dataset creation, management, and continuous improvement in response to researcher needs
Contribute to the integration of research-driven data advancements into production-ready systems
Stay informed on emerging data engineering and ML data management developments, bringing best practices to our systems