Research Scientist, Data

Pika

. Architect and implement large-scale data pipelines supporting model training and research workflows for text, image, audio, and video datasets .

Posted 9/24/2026full-timePalo Alto • California • United StatesMid-LevelSenior💰 $185,000 - $400,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting and implementing large-scale data pipelines for machine learning applications, with a strong focus on data quality, compliance, and the integration of multimodal models. Proficient in developing scalable data ingestion and management tools while ensuring ethical considerations throughout the data lifecycle.

Highest-signal resume keywords

Data Pipeline ArchitectureMachine Learning Data CurationDistributed Data SystemsPython ProgrammingCloud Data Platforms

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Hard Skills

Data Pipeline ArchitectureMachine Learning Data CurationDistributed Data SystemsPython ProgrammingSQLPySparkETL WorkflowsData Quality AssuranceDataset ManagementData Ingestion Tools

Soft Skills

Cross-Functional CollaborationProblem-SolvingCommunication

Tools & Technologies

SparkHadoopRayAWSGCPAzure

Industry Keywords

Data ComplianceData EthicsGenerative AIMultimodal ModelsData Lifecycle Management

Tech Stack

Tools & technologies

AWSAzureCloudETLGoogle Cloud PlatformHadoopPySparkPythonRaySparkSQL

About the role

Key responsibilities & impact

Architect and implement large-scale data pipelines supporting 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 privacy and ethical considerations 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
Stay informed about emerging data engineering and ML data management developments and apply best practices

Requirements

What you’ll need

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, such as Spark, Hadoop, Ray, or similar
Experience with efficient large dataset processing and 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 in Python, SQL, PySpark, or similar
Familiarity with cloud data platforms such as AWS, GCP, or 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
Ability to regularly work from the Palo Alto HQ
Authorization to work for any employer in the United States or ability to address US employment sponsorship requirements

Benefits

Comp & perks

Competitive salary and substantial equity in a high-growth startup
Full health benefits
401k matching
Collaborative, mission-driven team environment with major growth opportunities
Flexible on-site/remote hybrid work arrangement