Director, Scalable ML Systems & Infrastructure

Tubi; United States; Hybrid; USD 292,000 - 417,000; Full time

Benefits offered by this job

Annual bonus

Long-term incentive plan

Medical/dental/vision insurance

401(k) plan

Paid time off

Job summary

Tubi is seeking a Director of Machine Learning Engineering and Infrastructure to lead a hybrid team bridging ML engineering with infrastructure design. You will own the strategic direction for scaling ML capabilities and ensure distributed systems support innovation at massive scale.

This role requires 10+ years in ML engineering and distributed systems, 3+ years of leadership, and a strong background in end-to-end ML deployments, including recommendation and personalization at scale.

Qualifications

• 10+ years in ML engineering and distributed systems.
• 3+ years in leadership and management of technical teams.
• Proven end-to-end ML systems experience including recommendation/personalization.

Responsibilities

• Lead ML engineering and ML infrastructure teams.
• Define and execute the strategic roadmap for ML systems.
• Oversee design, development, and deployment of scalable ML pipelines.
• Architect distributed systems for ML workloads at scale.
• Partner with Product/Engineering/Content to align business goals.
• Foster experimentation, evaluation, and system monitoring best practices.
• Ensure cost efficiency, scalability and performance of ML infra.

Skills

Leadership

Communication

Strategy & Roadmapping

ML Engineering

Education

MSc or PhD in CS/ML or related

Tools

TensorFlow

PyTorch

AWS

Databricks

Job description

Tubi is seeking a Director of Machine Learning Engineering and Infrastructure to lead a hybrid team bridging ML engineering with infrastructure design. You will own the strategic direction for scaling ML capabilities and ensure distributed systems support innovation at massive scale.

This role requires 10+ years in ML engineering and distributed systems, 3+ years of leadership, and a strong background in end-to-end ML deployments, including recommendation and personalization at scale.