Staff Machine Learning Engineer - Recommendations

$145,600 - $270,400/Yr

Spectrum - Seattle, WA

Full-time - Senior

Seattle, WA

Telecommunications

About the position

The Staff Machine Learning Engineer for Recommendations at Warner Bros. Discovery will play a pivotal role in building and scaling the Direct-to-Consumer (DTC) personalization systems for the Max streaming app and future applications. This position requires a blend of technical expertise and leadership, focusing on architecting systems that enhance user experience through advanced recommendation algorithms. The engineer will collaborate with various teams to drive machine learning projects from inception to deployment, fostering a culture of innovation and experimentation.

Responsibilities

• Architect, build and scale a recommendation system for Max and other WBD offerings.
• Collaborate with ML/Ops engineers to enhance core components and infrastructure for model training and deployment.
• Drive ML projects from conception to completion in collaboration with data scientists, engineers, and product teams.
• Utilize and contribute to open-source cloud computing technologies.
• Support strategic planning and set goals for the engineering team.
• Mentor and influence engineers across organizations, showcasing high-quality work.
• Inspire a culture of experimentation and data-driven innovation.

Requirements

• 8+ years of industry experience, with 4+ years as a tech lead (preferred).
• Deep practical knowledge of large scale recommender systems or ML ranking/retrieval systems.
• Knowledge of large-scale distributed application architecture and performance tuning.
• Good practical knowledge in modern machine learning lifecycle.
• 5+ years of programming experience in Python/Java for rapid prototyping and production refinement.
• Ability to drive the roadmap for scalable production quality systems end-to-end.
• Excellent written and verbal communication skills, comfortable presenting to large audiences.
• Advanced degree (M.S. or Ph.D.) or equivalent industry experience in software engineering, computer science, machine learning, or related fields.
• Experience with cloud platforms like AWS, GCP, or Azure.
• Experience with CI/CD tools like GitHub Actions or Jenkins.