Staff AI Engineer

Create Music Group•Los Angeles, CA

5d•$190,000 - $210,000•Hybrid

About The Position

This Staff AI Engineer role sits within CMG's AI & ML Engineering organization at the intersection of AI systems architecture and full-stack product engineering. The role is the technical anchor for CreateOS's agentic AI layer, owning the design, deployment, and ongoing reliability of LLM-powered features and agent infrastructure across the platform. It focuses on building and scaling the agent platform; shipping production AI features end-to-end from data model to UI; and serving as the primary technical partner to product, M&A, A&R, and Marketing stakeholders. The emphasis is on production systems serving real users at scale, with the observability and reliability expected of revenue-critical software. The engineer helps establish architectural patterns and development standards for the broader AI team, mentors the ML Engineer, shapes tooling and primitives, and represents AI Engineering in cross-functional and executive forums. The role calls for high autonomy in deciding what to build and why. The position requires attending the Hollywood office in person 2–3 times per week. Interviews are a combination of virtual and in-person at the Hollywood office.

Requirements

At least 6 years of software engineering experience with a track record of shipping production systems.
At least 3 years of hands-on experience building production agentic AI or LLM-powered systems serving real users; prototypes and demos do not meet this requirement.
Demonstrated technical ownership of a non-trivial system or platform, including responsibility for its architecture.
Deep proficiency in a backend language such as Python or Node.js.
Working fluency in a modern frontend framework such as React or Next.js.
Strong experience with RAG systems, vector databases, and embedding-based retrieval, including tradeoffs in chunking, hybrid search, reranking, and freshness.
Experience designing evaluation frameworks for LLM systems, including output quality, hallucination detection, regression testing, and offline and online evaluation.
Experience designing and documenting RESTful APIs.
Proficiency with relational databases, particularly PostgreSQL, and comfort writing and optimizing SQL.
Solid understanding of containerization with Docker, Kubernetes, and CI/CD.
Proficiency with AI-native development tools such as Cursor and Claude Code.
Ability to operate with high autonomy and ambiguity, deciding what to build and why.
Must be able to attend in person at the Hollywood office 2–3 times per week.

Responsibilities

Architect AI agents and shared orchestration, tool-use, memory, and routing patterns, building toward a cohesive agent platform for CreateOS.
Design RAG pipelines, retrieval architectures, and semantic search grounded in CreateOS structured data, including contracts, royalty statements, catalog metadata, and deal terms.
Define guardrails, evaluation, observability, and human-in-the-loop standards so agentic systems ship safely and remain measurable.
Drive model, prompt, and tool-use choices, including production-scale cost and latency tradeoffs.
Integrate frontier LLMs, including OpenAI and Anthropic models, and selected open-source models into user-facing features across CreateOS modules.
Partner with product, M&A, A&R, and Marketing stakeholders to turn ambiguous business needs into well-scoped AI features.
Run discovery conversations with internal users to validate problems before committing engineering investment.
Translate non-technical requirements into technical specifications and surface cost, latency, accuracy, and scope tradeoffs early and explicitly.
Negotiate scope, timelines, and acceptance criteria with cross-functional partners.
Demo work in progress regularly and iterate based on real user feedback.
Document decisions and rationale to keep stakeholders aligned across long-running initiatives.
Ship full-stack AI-native features end-to-end, including chat interfaces, copilot tools, and workflow automation surfaces, from data model to UI.
Deploy and maintain production AI services to revenue-critical reliability, observability, and performance expectations.
Maintain CI/CD, testing standards, and code quality for the AI application layer.
Partner with Data Engineering to consume internal pipelines such as dbt and Airflow, third-party feeds such as DSPs and distributors, and event-driven flows into product surfaces.
Drive AI application-layer architecture decisions in collaboration with the VP of AI & ML Engineering.
Shape patterns, primitives, and tooling that help future hires ramp quickly and ship safely.
Mentor the ML Engineer and future AI hires, raising standards for code review, design documentation, and evaluation rigor.
Represent AI Engineering in cross-functional forums with product, data, and executive stakeholders.
Communicate tradeoffs clearly to engineering, product, and business audiences.