Staff Machine Learning Operations Engineer
2 days ago
SiriusXM is seeking a highly skilled Staff Machine Learning Operations Engineer to join our Data Platform Team. As a key player in our ML Ops team, you will be responsible for deploying, managing, and optimizing machine learning models, leveraging advanced tools like Databricks and MLFlow.
Key Responsibilities- Lead the deployment of ML models into production using Databricks and MLFlow, ensuring high performance and integration with existing systems.
- Continuously monitor and maintain the health of ML models, optimizing them for efficiency and effectiveness.
- Design and implement automated ML pipelines for model training, evaluation, and deployment, utilizing Databricks and MLFlow.
- Work closely with data scientists, engineers, and other stakeholders, providing ML Ops expertise and support.
- Spearhead the adoption of industry best practices in ML operations, focusing on areas like version control, data governance, and resource optimization.
- Stay ahead of the curve by researching and implementing emerging technologies and methodologies in the ML Ops field.
- Identify and resolve complex issues in ML model performance, deployment, and operational workflows.
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- 7+ years' experience in ML Ops, data engineering, or a similar role.
- Deep knowledge of ML models and algorithms.
- Expertise in Databricks, MLFlow, and other relevant ML tools and frameworks.
- Proficiency in programming languages such as Python, Scala, or Java.
- Strong experience with cloud platforms (AWS, Azure, etc.) and containerization technologies (Docker, Kubernetes).
- Excellent analytical, problem-solving, and communication skills.
- Must have legal right to work in the U.S.
SiriusXM is the leading audio entertainment company in North America, and the premier programmer and platform for subscription and digital advertising-supported audio products. Our platforms collectively reach approximately 150 million listeners, the largest digital audio audience across paid and free tiers in North America, and deliver music, sports, talk, news, comedy, entertainment, and podcasts.
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