Research Scientist, Data

Excerpt from the original posting

About the Role
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.

What You’ll Do
- 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

What We’re Looking For
- 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 (…

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