Staff AI Engineer

Established in 2015, Create Music Group is a leading music and entertainment company. The company operates as a record label, distribution company, and entertainment network which generates over 15 billion music streams each month on DSP’s. Named #2 on the Inc 5000 Fastest Growth Companies in America in 2020, the company has grown exponentially by leveraging its owned IP with its media and technology platform. The company works with superstar artists, major and independent record labels, and global media brands. It operates a number of companies including Label Engine, one of the largest independent music distribution platforms in the world, with over 75,000 artists and 5,000 label clients; and Flighthouse, a digital entertainment brand focused on Gen Z, which has more than 300 million followers across social media. Create Music Group is based in Hollywood, CA and has 400 employees worldwide.

Job Summary

This Staff AI Engineer role sits at the intersection of AI systems architecture and full-stack product engineering within CMG's AI & ML Engineering org. The person in this seat is the technical anchor for CreateOS's agentic AI layer — owning the design, deployment, and ongoing reliability of the LLM-powered features and agent infrastructure that run across the platform.

Day-to-day, this role spans three areas: building and scaling the agent platform (orchestration, RAG pipelines, memory, routing, guardrails); shipping production AI features end-to-end from data model to UI; and serving as the primary technical voice with product, M&A, A&R, and Marketing stakeholders. Unlike a research or prototype role, the bar here is production — systems serving real users at scale, with the observability and reliability expectations of revenue-critical software.

Beyond individual contribution, this engineer helps set the architectural patterns and development standards the broader AI team builds against — mentoring the ML Engineer, shaping tooling and primitives, and representing AI Engineering in cross-functional and executive forums. It's a high-autonomy seat for someone who's comfortable deciding what to build and why, not just how.

The right candidate brings 6 years of software engineering experience, at least 3 of which are hands-on with production agentic AI or LLM systems, along with deep fluency in RAG architectures, eval frameworks, and modern AI-native development practices.

Responsibilities

Agent Platform & LLM Systems

• Architect AI agents and the orchestration, tool-use, memory, and routing patterns they share — building toward a cohesive agent platform for CreateOS
• Design RAG pipelines, retrieval architectures, and semantic search grounded in CreateOS structured data (contracts, royalty statements, catalog metadata, deal terms)
• Define guardrails, evaluation, observability, and human-in-the-loop standards so agentic systems ship safely and stay measurable
• Drive model, prompt, and tool-use choices — including cost and latency tradeoffs at production scale
• Integrate frontier LLMs (OpenAI, Anthropic) and selected open-source models into user-facing features across CreateOS modules

Stakeholder Partnership & Requirements

• 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 the problem before committing engineering investment
• Translate non-technical requirements into clear technical specs; surface tradeoffs (cost, latency, accuracy, scope) early and explicitly
• Negotiate scope, timelines, and acceptance criteria with cross-functional partners
• Demo work-in-progress regularly; iterate based on real user feedback rather than assumed requirements
• Document decisions and rationale so stakeholders stay aligned across long-running initiatives

Production Engineering

• Ship full-stack AI-native features end-to-end — chat interfaces, copilot tools, workflow automation surfaces — from data model to UI
• Deploy and maintain production AI services with the reliability, observability, and performance expectations of a revenue-critical system
• Maintain CI/CD, testing standards, and code quality for the AI application layer
• Partner with Data Engineering to consume internal pipelines (dbt, Airflow), third-party feeds (DSPs, distributors), and event-driven flows into product surfaces

Technical Direction & Influence

• Drive architecture decisions for the AI application layer in collaboration with the VP of AI & ML Engineering
• Help shape the patterns, primitives, and tooling the team builds against — so future hires ramp fast and ship safely
• Mentor the ML Engineer and future AI hires; raise the bar on code review, design docs, and eval rigor
• Represent AI engineering in cross-functional forums with product, data, and executive stakeholders
• Communicate tradeoffs clearly across engineering, product, and business audiences

Qualifications

• 6 years of software engineering experience with a track record of shipping production systems
• 3 years hands-on building production agentic AI or LLM-powered systems — not prototypes, not demos, systems serving real users
• Demonstrated technical ownership of a non-trivial system or platform — you've been the person responsible for the architecture, not just the implementer
• Deep proficiency in a backend language (Python or Node.js) and working fluency in a modern frontend framework (React, Next.js)
• Strong experience with RAG systems, vector databases, and embedding-based retrieval — including design tradeoffs (chunking, hybrid search, reranking, freshness)
• Experience designing eval frameworks for LLM systems — output quality, hallucination detection, regression testing, offline online eval
• Experience designing and documenting RESTful APIs
• Proficiency in relational databases (PostgreSQL); comfortable writing and optimizing SQL
• Solid understanding of containerization (Docker), Kubernetes, and CI/CD
• Proficiency with AI-native development tools (Cursor, Claude Code)
• Ability to operate with high autonomy and ambiguity — you decide what to build and why, not just how

Pay Scale

• $190,000 - 210,000 / year
• The final compensation within this range will be determined based on the candidate’s experience, skills, and overall fit for the role.

(Add for Interns)

Details

• Hybrid role: Must be able to attend in person in the LA office 2-3 times per week
• Interviews will be a combination of virtual and in-person.

Fair Chance Policy

In accordance with the Los Angeles County Fair Chance Ordinance, we will consider employment for qualified applicants with criminal histories. We evaluate candidates based on their qualifications and the nature of the offense in relation to the job for which they are applying.