Job Summary We are building CreateOS — a next-generation operating system for modern record labels — and AI is at the center of it. As a Machine Learning Engineer, you will be a core builder on the team responsible for designing, training, deploying, and maintaining the ML systems that power intelligent automation, forecasting, anomaly detection, and agentic workflows across the platform. This is a hands-on engineering role for someone who is equally comfortable prototyping a new model and shipping it to production. You will work directly with the VP of AI & ML Engineering and partner closely with Data Engineering, Analytics, and Product to move AI from experimentation into reliable, scalable production systems embedded in real user workflows. Your immediate impact will be on three of CMG's highest-priority AI initiatives: M&A catalog valuation forecasting, AI-driven A&R discovery, and marketing automation agents — each directly tied to revenue growth and competitive differentiation. Responsibilities Applied Machine Learning - Build and maintain ML models for forecasting, anomaly detection, classification, ranking, and optimization across music industry use cases (catalog valuation, royalty reasoning, A&R intelligence, marketing performance, etc.) - Partner with Analytics & BI to identify, engineer, and validate features that drive meaningful predictive power - Own the full ML lifecycle — from problem framing and data exploration through training, evaluation, deployment, and monitoring MLOps & Production Engineering - Deploy and monitor models in production using modern MLOps tooling - Instrument models for performance tracking, drift detection, and continuous improvement - Implement CI/CD, automated testing, model versioning, and observability for all ML systems - Collaborate with Data Engineering to ensure data quality, feature delivery, and pipeline reliability Agentic AI & LLM Systems (Stretch) - Develop and maintain modular AI agents that automate multi-step workflows across CreateOS (contracts, accounting, distribution, metadata) - Build and iterate on RAG pipelines, retrieval architectures, and semantic search systems grounded in structured business data - Implement guardrails, evaluation frameworks, and safe action boundaries for agentic systems Collaboration & Communication - Translate business problems from non-technical stakeholders into well-scoped ML solutions - Document model design decisions, evaluation results, and known limitations clearly - Contribute to a culture of engineering rigor and responsible AI development - Other duties as assigned Qualifications - 4+ years of software engineering experience in a production environment, with exposure to ML or data science work (academic, professional, or project-based); OR 2+ years of experience specifically as an ML Engineer or Applied Data Scientist - Strong proficiency in Python and ML frameworks (PyTorch, scikit-learn, XGBoost, or similar) - Hands-on experience building, deploying, and monitoring models in cloud environments — GCP strongly preferred (AWS or Azure acceptable); familiarity with services such as Vertex AI, BigQuery, Cloud Functions, and Cloud Run is a strong plus - Solid understanding of modern ML techniques — supervised/unsupervised learning, time series forecasting, embeddings, ranking — and their mathematical foundations - Experience with LLMs and prompt engineering, including building RAG systems or LLM-powered features - Comfortable working with structured and unstructured data at scale - Strong communication skills with the ability to explain complex model behavior to non-technical audiences Preferred Qualifications - MS in Machine Learning, Data Science, Business Analytics, Computer Science, Statistics, or a related quantitative field; OR 4+ years of experience as an ML Engineer or Applied Data Scientist in lieu of an advanced degree - Experience at a startup or high-growth technology company where you owned features end-to-end - Familiarity with agentic frameworks (LangChain, LangGraph, AutoGen, or similar) - Background in music, media, entertainment, or rights/royalties data - Experience with ontology design, knowledge graphs, or semantic data modeling - Contributions to open-source ML projects or published research - Familiarity with AI evaluation practices — hallucination detection, bias auditing, model explainability Pay Scale - $160,000 - $200,000/USD per year - The final compensation within this range will be determined based on the candidate’s experience, skills, and overall fit for the role. 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.

• Design and build data pipelines for agent training: collection, filtering, deduplication, formatting, and versioning across text, image, and multimodal sources • Build and maintain infrastructure for efficient data loading, storage, and retrieval at scale (S3, distributed systems, streaming pipelines) • Collaborate with research scientists to translate research requirements into concrete data specifications, and iterate as experiments reveal new needs • Create evaluation datasets and benchmarks in collaboration with researchers—curating task distributions that surface real failure modes • Develop tooling for dataset construction—including human annotation workflows, synthetic data generation, and preference data collection for RLHF/DPO-style training • Own data quality: build validation frameworks, monitor for drift and contamination, and establish standards that make datasets trustworthy and reproducible • Document datasets thoroughly: provenance, known limitations, intended use cases, and versioning history • Implement comprehensive test coverage for data pipelines and ML workflows, ensuring reliability and catching regressions early • Elevate codebase quality through code reviews, refactoring, and establishing engineering best practices that help research velocity scale sustainably • Contribute to team roadmaps by identifying data bottlenecks and proposing solutions that unblock research velocity

• Lead research and development of pCTR and conversion prediction models, with a focus on improving calibration, reducing training data biases (selection bias, position bias, optimizer’s curse), and advancing model accuracy across Instacart’s ads surfaces. • Design and implement debiasing techniques such as Mixed Negative Sampling (MNS), Inverse Propensity Weighting (IPW), counterfactual risk minimization, and calibration methods (Platt scaling, isotonic regression) to address systematic prediction biases. • Contribute to the next-generation Multi-Domain Multi-Task (MDMT) model architecture, incorporating innovations like Mixture-of-Experts (MoE), Transformer layers for sequential user behavior, and LoRA adaptors for scalable domain fine-tuning. • Drive sequence modeling initiatives including the TIGER generative retrieval system and Semantic ID representation learning, expanding their application across ads surfaces such as Product Details, Search and other placements. • Collaborate with the broader ML community in the company on the path toward Foundation Models using autoregressive user behavior prediction. • Formulate and scope ambiguous modeling problems from first principles. Translate business observations (e.g., overcalibration patterns, cold-start underperformance) into well-defined ML research directions with clear evaluation criteria. • Publish and present findings internally. Contribute to the team’s culture of technical rigor through design reviews, paper sharing, and experiment retrospectives.

• Work on strategic use cases, translating business needs into data-driven analytical solutions • Conduct end-to-end analyses: exploration, understanding, data preparation, and delivery • Work with large volumes of data, ensuring quality, integrity, and statistical consistency • Develop, validate, and iterate statistical and Machine Learning models, ensuring performance, stability, and explainability • Monitor model performance metrics, proposing continuous improvements • Contribute to building and evolving Feature Stores, promoting standardization and governance • Create and maintain notebooks and pipelines in Databricks, following analytical engineering best practices • Collaborate with multidisciplinary teams (Engineering, Governance, BI, and Business) • Communicate results, hypotheses, and insights clearly and strategically, using data visualization and storytelling