Staff Software Engineer, Machine Learning

Warner Bros. Discovery

full-time

Posted on: 9/22/2025

Origin: • 🇺🇸 United States • New York

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💰 $145,600 - $270,400 per year

Lead

AWSCloudFFmpegGoGoogle Cloud PlatformGrafanaMicroservicesPrometheusPythonTerraform

About the role

Design and lead development of core components of the machine learning platform with focus on scalability, reliability, and cloud-agnostic principles
Build APIs and microservices that power end-to-end machine learning workflows, including model deployment, orchestration, and observability
Collaborate with content protection and security teams to ensure robust access controls and compliance
Own cloud infrastructure and CI/CD automation using Infrastructure-as-Code (IaC) principles
Champion engineering excellence through well-tested code, clear documentation, and continuous improvement of development and deployment practices
Establish and enforce best practices in testing, code quality, and monitoring for ML pipeline components
Work closely with Video AI engineers and product managers to support use cases like scene segmentation, video annotation, clip generation, and metadata enrichment

7+ years of software engineering experience building scalable backend systems
Deep experience with cloud platforms (AWS, GCP); ability to design for cloud-agnostic deployment
Experience working with production deployment of machine learning systems
Proficiency in Python and Go
Strong software design and architecture skills
Proven experience implementing and managing CI/CD workflows and IaC with tools like Terraform or CloudFormation
Familiarity with Agile development practices and a strong operational mindset
Experience in building and maintaining observability stacks (e.g., Prometheus, Grafana, OpenTelemetry)
Strong understanding of media processing and video engineering concepts including transcoding, segmentation, and frame manipulation, using libraries such as FFMPEG, OpenCV, and Demux
Experience collaborating in a cross-functional team and setting engineering standards at scale