We are UMG, the Universal Music Group. We are the world’s leading music company. In everything we do, we are committed to artistry, innovation and entrepreneurship. We own and operate a broad array of businesses engaged in recorded music, music publishing, merchandising, and audiovisual content in more than 60 countries. We identify and develop recording artists and songwriters, and we produce, distribute and promote the most critically acclaimed and commercially successful music to delight and entertain fans around the world.
How you’ll LEAD:
As a Machine Learning Engineer, you will be a critical member of the data management and platform development team. You will be responsible for designing, developing, and managing the machine learning platforms, applying MLOps & model lifecycle best practices. Additionally, you’ll work with data scientists, data engineers, and product leads to develop, operationalize, and maintain machine learning models that provide action-on, data-driven insights regarding fan behavior. You’ll work in a collaborative Agile environment using the latest in engineering best practices with involvement in all aspects of the software and model development lifecycle. You will primarily develop on Google Cloud Platform and other technologies such as BigQuery, Airflow, Python, SQL, Spark/PySpark, Linux, Docker, and Kubernetes.
Please note, this position is located in Charlotte, NC / Remote.
How you’ll CREATE:
- Work with data scientists and data engineers to gather and understand requirements for designing new, or scaling existing, machine learning systems/models
- Design, develop, and maintain the MLOps systems/platforms used to manage the machine learning model lifecycles
- Maintain, monitor, and improve existing production machine learning models over time (e.g. combating drift, adding features, etc.)
- Build and deploy machine learning systems on Google Cloud to enable AI and ML capabilities
- Proactively suggest, plan, and integrate new tools and methods to improve productivity/efficiency, scale system designs, and reduce costs
Bring your VIBE:
- Bachelor’s degree or equivalent experience in Computer Science or related field
- 2+ years' experience as a machine learning engineer – or equivalent – designing large scale machine learning systems from existing data science / machine learning models
- Knowledge of MLOps principles (model deployment, monitoring, life cycle management, etc.)
- Experience in at least one programming language (Python strongly preferred)
- Experience using data science/machine learning technologies such as H2O or Scikit-learn (H2O preferred)
- Hands-on experience writing performant SQL queries, working with large datasets and related technologies, and using big data tools such as Spark/PySpark
- Experience using version control systems (Git strongly preferred) and working in a remote software development environment
- Strong analytical, problem solving, and interpersonal skills, desire to learn, and ability to operate in a self-guided manner in a fast-paced rapidly changing environment
- Preferred: Experience using or deploying MLOps systems/tooling (e.g. MLFlow)
- Preferred: Experience working with columnar datastores (BigQuery preferred)
- Preferred: Experience in pipeline orchestration (e.g. Airflow)
- Preferred: Experience using Google Cloud Platform services
- Preferred: Experience in DevOps processes/tooling (CI/CD, GitHub Actions)
- Preferred: Experience using infrastructure as code frameworks (Terraform)
- Competitive Compensation Package including Salary, Benefits and Generous 401k Savings Plan
- Paid Time Off – Paid Holidays, Winter Break, Summer Fridays
- Student Loan Repayment Assistance
- Employee Developmental Support
- Annual Gym Reimbursement Package
Universal Music Group is an Equal Opportunity Employer
“All UMG employees are currently required to be fully vaccinated against COVID-19 before entering any Company offices unless they have been approved for an exemption or unless prohibited by applicable law”
Disclaimer: This job description only provides an overview of job responsibilities that are subject to change.