The New York Times

ML Ops Engineer, Machine Learning & AI

The New York Times

New York•On-site•2-5 Yrs•2 hours ago

The mission of The New York Times is to seek the truth and help people understand the world.

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

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
Hands-on experience with containerization and orchestration (e.g., Docker, Kubernetes)
Cloud platform experience (AWS, GCP) and familiarity with infrastructure-as-code (e.g., Terraform, CloudFormation)
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)

Browse Jobs