ML Ops Engineer, Machine Learning & AI

About This Role

As an MLOps Engineer, you will join the Machine Learning & AI team to build and maintain the infrastructure that powers the machine learning lifecycle. This role involves automating and refining the training, deployment, monitoring, and management of ML models, enhancing the experience of digital readers and growing our subscriber base.

Responsibilities

• Build and Automate ML Pipelines by owning robust CI/CD pipelines for automated model training, validation, deployment, and retraining.
• Productionalize Models by building processes for packaging, containerizing, and deploying ML models as scalable, low-latency, and highly-available services.
• Implement and manage comprehensive monitoring for production models, tracking system health, data drift, and model performance degradation.
• Manage and evolve our MLOps toolchain, including model registries, feature stores, experiment tracking systems, and model serving platforms.
• Partner with data scientists to understand model requirements and optimize them for production, and support software engineers in integrating with ML services.
• Champion and enforce MLOps best practices for reproducibility, versioning (data, code, model), testing, and governance.
• Demonstrate support and understanding of journalistic independence and commitment to the mission of seeking truth and helping people understand the world.

Requirements

• 2+ years of software engineering or DevOps experience with a focus on MLOps, automation, and infrastructure
• 2+ years of experience programming in Python or Go
• Experience building and managing CI/CD pipelines (e.g., Github Actions, Jenkins, GitLab CI)
• Hands-on experience with containerization and orchestration (e.g., Docker, Kubernetes)
• Cloud platform experience (AWS, GCP)
• Familiarity with infrastructure-as-code (e.g., Terraform, CloudFormation)

Qualifications

• 2+ years of software engineering or DevOps experience with a focus on MLOps, automation, and infrastructure; 2+ years of experience programming in Python or Go

Nice to Have

• Experience with MLOps tools (e.g., MLflow, Kubeflow)
• Experience with the machine learning model lifecycle, from experimentation to production
• Experience with data processing frameworks (e.g., Spark, Dask, or Ray)
• Experience with low-latency no-sql datastores (BigTable, Dynamo, etc)
• Familiarity with monitoring and observability stacks (e.g., Prometheus, Grafana, Datadog, or ELK)
• Knowledge of data engineering pipelines and orchestration tools (e.g., Airflow, Prefect)

Skills

* Required skills

Benefits