Senior ML Engineer, Personalization & Recommendations

Tubi; New York (NY); On-site; USD 150,000 - 240,000; Full time

Benefits offered by this job

Healthcare Coverage

Competitive Compensation

Parental Leave

Wellness Programs

Education Reimbursement

Retirement Savings Matches

Job summary

Tubi is seeking an experienced Machine Learning Engineer to advance our video personalization and recommendation systems. You will design and deploy end-to-end ML pipelines, from data extraction to model deployment, leading complex initiatives across regions and product areas.

Ideal candidates have 8+ years in production ML, strong expertise in TensorFlow or PyTorch, and advanced degrees in CS/ML/math. You will collaborate with Product, Engineering, and Data Science teams to improve user

Qualifications

• Experience with deep learning technologies for recommendation systems.
• Solid understanding of statistical concepts and performance evaluation metrics for ML.
• Ability to debug, optimize, and scale complex ML pipelines.

Responsibilities

• Lead the design, development, and deployment of advanced recommendation systems for a global audience.
• Design and implement scalable ML pipelines: data extraction, feature development, model training, testing, and deployment.
• Collaborate with Product, Engineering, and Data Science teams to align on requirements and deliver ML-driven solutions.
• Monitor, evaluate, and optimize deployed models to meet business goals and user experience targets.

Skills

Machine Learning

Deep Learning

Education

MSc/PhD in CS/ML/Stats/Math

Tools

TensorFlow

PyTorch

Job description

Tubi is seeking an experienced Machine Learning Engineer to advance our video personalization and recommendation systems. You will design and deploy end-to-end ML pipelines, from data extraction to model deployment, leading complex initiatives across regions and product areas.

Ideal candidates have 8+ years in production ML, strong expertise in TensorFlow or PyTorch, and advanced degrees in CS/ML/math. You will collaborate with Product, Engineering, and Data Science teams to improve user