Staff Machine Learning Engineer

Dublin, Ireland

Regular Employee Full-Time

R-2026-03-62

Hybrid

Who we are:

SiriusXM and its brands (Pandora, SiriusXM Media, AdsWizz, Simplecast, and SiriusXM Connect) are leading a new era of audio entertainment and services by delivering the most compelling subscription and ad-supported audio entertainment experience for listeners in the car, at home, and anywhere on the go with connected devices. Our vision is to shape the future of audio, where everyone can be effortlessly connected to the voices, stories and music they love wherever they are.

This is the place where a diverse group of emerging talent and legends alike come to share authentic and purposeful songs, stories, sounds and insights through some of the best programming and technology in the world. Our critically acclaimed, industry-leading audio entertainment encompasses music, sports, comedy, news, talk, live events, and podcasting. No matter their individual role, each of our employees plays a vital part in bringing SiriusXM’s vision to life every day.

We are proud to be launching a new state-of-the-art technology facility in Dublin, Ireland. The Dublin-based team will play a critical role in our continued digital transformation and will function as a center of excellence for SiriusXM’s global Product and Technology organization.

SiriusXM’s new Dublin facility will comprise a workforce primarily focused on software development, automotive technology and engineering, AdTech, data science, and analytics. Talent based in the new technology hub will be charged with developing and implementing best-in-class standards for high-quality, scalable software deliveries for SiriusXM’s streaming and in-vehicle audio entertainment platforms.

How you'll make an impact:

As a Staff ML Engineer at SiriusXM, you will bring machine learning models from development and experimentation to reliable, scalable production systems. You will work closely with Data Scientists, Data Engineers, Platform Engineers, and Services teams to productionize models, build robust feature pipelines, and ensure models operate reliably in live environments. This is a hands-on engineering role with ownership across deployment, serving, monitoring, and lifecycle management of ML systems.

What you'll do:

Model Productionization and Deployment

Lead the deployment of ML models into production using Databricks and MLFlow, ensuring high performance and integration with existing systems.

Implement scalable and reliable model serving solutions (both batch and real-time).

Design and maintain CI/CD pipelines for automated build, test, validation, and deployment of ML artifacts.

Support safe rollout and rollback strategies across environments.

Feature Engineering and Serving

Translate feature definitions from data scientists into production-grade pipelines.

Build and maintain reliable feature serving systems to ensure consistency between training and inference environments.

Optimize feature pipelines for performance, maintainability, and scalability.

Model Lifecycle Management

Support model registration, versioning, publishing, and promotion.

Implement retraining pipelines and redeploy improved models as data and algorithms evolve.

Participate in the decommissioning and retirement of models.

Reliability, Observability, and Performance

Ensure production models meet latency, throughput, and reliability requirements.

Implement logging, monitoring, and alerting of ML systems.

Investigate and resolve issues related to model performance, deployments, and infrastructure.

Contribute to operational runbooks and documentation.

Cross-Functional Collaboration

Work closely with data scientists, engineers, and other stakeholders, providing ML engineering and operations expertise and support.

Best Practices Implementation

Spearhead the adoption of industry best practices in ML engineering and operations, focusing on areas like version control, data governance, and resource optimization.

Problem Resolution

Identify and resolve complex issues in ML model performance, deployment, and operational workflows.

What you'll need:

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.

7+ years of experience in ML Engineering, MLOps, data engineering, or a similar role.

Deep knowledge of ML models and algorithms.

4+ years of experience with 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).

Familiarity with infrastructure-as-code tooling and practices, e.g., CDK, Terraform

Excellent analytical, problem-solving, and communication skills.

The requirements and duties described above may be modified or waived by the Company in its sole discretion without notice.

R-2026-03-62