Staff Engineer, Storage Metadata and Context

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 MCPsImplement 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 protocolsAPI & 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 LLMsMetadata Engineering:
Proven ability to design schemas and indexing pipelines using Kafka, Elastic Search, or Vector DBs to represent physical files as semantic entities3+ 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 workersHPC Awareness:
Understanding of the performance characteristics of high-end storage (Vast, Net App, Storage Grid, 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 $ to $ per year. The base pay actually offered will take into account…