Senior Machine Learning Engineer - Artist-First AI Music Lab
Build and scale production machine-learning pipelines and evaluation systems powering generative AI music experiences. The role requires hands-on LLM, prompt-engineering, data-pipeline, cloud, and user-facing product experience.
About the job
Responsibilities
- Design, build, evaluate, and improve machine learning training and inference pipelines for AI-driven music experiences.
- Apply machine learning and prompt engineering across complex pipelines involving large language models.
- Create evaluation frameworks, including LLM-as-judge pipelines, to measure quality and enable rapid iteration.
- Partner with music subject-matter experts to develop training and reference data through synthetic generation, expert curation, and taxonomy design.
- Build scalable systems balancing experimentation velocity with production rigor, performance, reliability, and latency.
- Collaborate with Data Science teams to connect evaluation frameworks with real-world usage signals and improve model quality.
- Contribute to technical direction and engineering practices for model deployment, observability, experimentation, and production infrastructure.
- Work cross-functionally with engineering, product, design, and music-industry partners to shape listening experiences.
Requirements
- Experience applying machine learning in production environments.
- Hands-on experience with large language models, prompt engineering, evaluation systems, and shipping LLM-driven features to production.
- Experience building and maintaining production ML systems using Python, Java, Scala, or similar languages.
- Experience building large-scale data pipelines for sourcing, preparing, and evaluating training data.
- Experience with cloud platforms such as Google Cloud, AWS, or Azure.
- Ability to explain machine learning concepts, assumptions, and trade-offs to technical and non-technical audiences.
- Experience building user-facing products and exercising judgment around conversational AI and generative user experiences.
- Strong experimentation, iteration, collaboration, and data-informed decision-making skills.
Skills
Machine Learning, LLMs, Prompt Engineering, Llm Evaluation, Python, Java, Scala, Data Pipelines, GCP, AWS, Azure, Model Deployment, Observability, Generative AI
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