Senior Machine Learning Operations Engineer

About the role

Become a Senior Machine Learning Operations Engineer at Paramount, where you'll own critical aspects of model lifecycle management, monitoring, and incident response. Your responsibilities include ensuring model traceability, building end-to-end monitoring systems, collaborating with data engineering on data quality, detecting issues proactively, and owning incident response for ML systems.

Responsibilities

• Own model traceability: Ensure every model in production has clear lineage and adopt recommended tooling for versioning, metadata, and model registry.
• Build end-to-end monitoring: Monitor the entire signal path, from data arrival to serving latency, and own this responsibility.
• Partner with Data Engineering: Collaborate to identify and address data quality issues, detect drift in upstream sources, and maintain feature freshness.
• Detect issues proactively: Track drift over time, flag slow degradation, and surface feature freshness problems early.
• Build diagnostic tooling: Quickly diagnose and resolve issues by logging relevant context and building dashboards to tie it together.
• Own incident response for ML systems: Develop rollback playbooks, hotfix strategies, and automated gates to block bad deployments. Conduct post-mortems and close gaps.
• Coordinate on post-deployment metrics: Work with ML engineers, data engineers, and stakeholders to define and collect metrics after deployment.

Requirements

• 5+ years in ML engineering, applied ML, or a related ML role, with experience in monitoring, reliability, deployment, or incident response.
• Experience building or operating model registries, ML monitoring systems, or production ML pipelines.
• Understanding of ML systems end-to-end, including the infra layer and its impact on feature freshness and model behavior.
• Robust SQL skills and comfort with data distributions, feature health, and model behavior.
• Ability to partner with DevOps and Platform teams to define infrastructure needs without needing to own the infrastructure.
• Experience operating recommendation or personalization systems at scale.

Qualifications

• Master's degree in Computer Science, Statistics, Applied Mathematics, or a related field.
• Proven track record of success in ML operations roles.
• Strong problem-solving and analytical skills.
• Excellent communication and collaboration skills.

Skills

• Python, R, or other programming languages commonly used in ML.
• Experience with cloud platforms like AWS, Google Cloud, or Azure.
• Knowledge of Kubernetes, Docker, and container orchestration tools.
• Experience with monitoring tools such as Prometheus, Grafana, or ELK stack.
• Experience with CI/CD pipelines and automation tools.

Benefits

• Hiring Salary Range: $139,200.00 - 208,800.00.
• Generous paid time off.
• Medical, dental, vision, 401(k) plan, life insurance coverage, disability benefits, tuition assistance program, and PTO.
• Opportunities for on-site and virtual engagement events.
• Unique opportunities to make meaningful connections and build a vibrant community.

Pay

Hiring Salary Range: $139,200.00 - 208,800.00.

Schedule

Full-time.