Staff Engineer, Storage Metadata and Context

Disney; Emeryville (CA); On-site; USD 183,000 - 237,000; Full time

Job summary

Pixar Animation Studios seeks a Staff Engineer to bridge storage systems and AI workflows. You will design APIs, metadata services, and MCP implementations to let AI agents navigate petabytes of media at scale, with a focus on context-aware storage for production.

You’ll work across REST, gRPC, and MCP, collaborating with storage, software tools, and creative teams to convert complex creative needs into precise metadata queries and scalable pipelines.

Qualifications

• Bridge data center and AI agent workflows for metadata pipelines.
• Expertise in NFS, SMB, and S3 protocols and related storage concerns.
• APIs (REST, gRPC) and middleware between storage and apps.
• Understanding of MCP and tooling for LLMs.
• Design schemas and indexing with Kafka, ElasticSearch, or Vector DBs.
• 3+ years in software-driven infrastructure and scale environments.
• Translate abstract creative needs into metadata queries.
• Backend development across languages; Python is a plus.

Responsibilities

• Architect Semantic Connectivity: expose NAS (NFS/SMB/S3) to AI agents via MCPs and APIs.
• Implement Model Context Protocol servers for safe data browsing by models.
• Design automated systems to interpret long-path hierarchies and headers.
• Bridge storage teams, tool designers and artists to enable workflows.
• Manage metadata for raw data and its digital twin across the library.
• Enable smart features like auto-tagging and similarity search.
• Participate in on-call rotation for critical issues.

Skills

Full-Stack Storage

Protocol Knowledge

APIs & Middleware

MCP & AI Tooling

Metadata Engineering

Software-Driven Infra

Translator Mindset

Python Proficiency

Tools

Kafka

ElasticSearch

Vector DBs

Docker

Kubernetes

REST

gRPC

Job description

Staff Engineer, Storage Metadata and Context

Job ID R-04164 Location Emeryville, California, United States Business Pixar Animation Studios Date posted Nov. 03, 2026

Every great creative project needs a spark—and we’re the team that collaborates to support and organize the digital media behind the magic. As our data scales, we want to close a growing context gap between our technical storage hierarchies and the natural language of our creative teams. As a Staff Engineer at the intersection of Data Platforms and AI, you will architect the software layers that enable our production-facing storage to become context-aware. Your mission is to design the APIs, Metadata services, and MCP implementations that allow AI agents to navigate and reason across Petabytes of high-value media assets. You will help to bridge the gap between traditional HPC protocols and a modern Agentic ecosystem.

RESPONSIBILITIES:

• Architect Semantic Connectivity: Develop and maintain the software layer that exposes existing NAS environments (NFS, SMB, S3) to AI agents and LLM frameworks through modern API abstractions and MCPs


• Implement Model Context Protocol (MCP): Lead the implementation of MCP servers to provide a standardized interface for AI models to safely and efficiently browse, read, and interpret data within our HPC storage


• Semantic Discovery & Agentic Connectivity: Design automated systems to deconstruct the implicit context stored in long-path hierarchies and file headers, architecting the "connective tissue" (REST, gRPC, MCP) that enables AI agents to resolve complex creative queries without needing direct knowledge of underlying mount paths or protocols


• Cross-Disciplinary Collaborator: Act as a critical "bridge" across technical storage teams, software tool designers, and creative artists—adapting your communication style to translate deeply technical concepts into practical, workflow-enhancing solutions for non-technical users


• Data & Metadata Orchestration: Manage the state and relationship between raw file data and its digital twin in the metadata layer, ensuring that relational and semantic data remain in sync as the studio library evolves


• Context Feature Enablement: Enable "smart" features within existing storage such as auto-tagging, similarity search, and automated summarizing


• On-call: Ability to participate in an on-call rotation for critical issues

QUALIFICATIONS:

• Full-Stack Storage Expertise: Ability to bridge the gap between the data center and the AI agent. You leverage your background in architecting, managing, and troubleshooting physical and/or virtual storage solutions to inform how agentic workflows and metadata pipelines access underlying data at petabyte scale


• Broad Protocol Knowledge: Expertise in how data is accessed and managed across NFS, SMB, and S3. You understand the nuances of file-locking, permissions, and latency across different storage protocols


• API & Middleware Development: Proven experience building APIs (REST, gRPC) and middleware that sit between raw storage and application layers


• Model Context Protocol (MCP) & AI Tooling: Understanding of MCP and how to build tool-calling interfaces for LLMs


• Metadata Engineering: Proven ability to design schemas and indexing pipelines using Kafka, ElasticSearch, or Vector DBs to represent physical files as semantic entities


• 3+ Years of Software-Driven Infrastructure: Recent experience in scale environments where you've transitioned manual workflows into programmatic, API-driven services


• The Translator Mindset: Ability to translate abstract creative needs (e.g., "I need to see all iterations of this scene") into technical metadata queries that return precise results


• Versatile Backend Development: High degree of comfort working across various programming languages to build the foundational "glue" that connects storage metrics and file data to agentic frameworks; deep proficiency in Python is a significant plus

PREFERRED SKILLS:

• Modern AI Frameworks: Hands-on experience with agentic libraries, with a focus on building custom data connectors and Retrieval-Augmented Generation (RAG) architectures


• Containerization & Orchestration: Experience with Docker and Kubernetes for scaling metadata extraction and embedding workers


• HPC Awareness: Understanding of the performance characteristics of high-end storage (Vast, NetApp, StorageGrid, AWS) and how to minimize the overhead of metadata operations


• Creative Pipeline Interest: A passion for digital content creation and an interest in how AI can accelerate and modernize production workflows

The hiring range for this position in Emeryville is $182,835.00 to $236,610.00 per year. The base pay actually offered will take into account internal equity and also may vary depending on the candidate’s geographic region, job-related knowledge, skills, and experience among other factors.

Pixar Animation Studios, a wholly owned subsidiary of The Walt Disney Company, is an Academy Award®-winning film studio with world-renoinswned technical, creative and production capabilities in the art of computer animation. The Northern California studio has created some of the most successful and beloved animated films of all time, including “Toy Story,” “Monsters, Inc.,” “Cars,” “The Incredibles,” “Ratatouille,” “WALL•E,” “Up,” “Toy Story 3,” “Brave,” “Inside Out,” and “Coco.” Its movies have won 35 Academy Awards® and have grossed more than $13 billion at the worldwide box office to date.

About The Walt Disney Company:

The Walt Disney Company, together with its subsidiaries and affiliates, is a leading diversified international family entertainment and media enterprise that includes three core business segments: Disney Entertainment, ESPN, and Disney Experiences. From humble beginnings as a cartoon studio in the 1920s to its preeminent name in the entertainment industry today, Disney proudly continues its legacy of creating world-class stories and experiences for every member of the family. Disney’s stories, characters and experiences reach consumers and guests from every corner of the globe. With operations in more than 40 countries, our employees and cast members work together to create entertainment experiences that are both universally and locally cherished.

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