Staff & Principal Machine Learning Engineer

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

• We are seeking a highly skilled Machine Learning Engineer to contribute to transformative projects in video personalization
• In this role, you will design and implement advanced algorithms and systems to improve our personalization strategy
• As a senior technical expert, you will tackle complex problems in machine learning at scale, collaborating closely with cross-functional teams to develop and optimize machine learning-driven solutions
• Lead the design, development, and implementation of advanced recommendation systems and algorithms for a global audience
• Conduct deep dives into algorithmic components and systems, ensuring that models are optimized for both performance and scalability across multiple regions and product areas
• Build and deploy high-impact robust ML pipelines, including data extraction, feature development, model training, testing, and deployment
• Continuously monitor, evaluate, and optimize the performance of deployed models, ensuring they meet business goals and provide high-quality user experiences
• Work closely with Product, Engineering, and Data Science teams to align on product requirements, set expectations, and deliver machine learning-driven solutions that improve user engagement

Benefits

• Healthcare Coverage: We offer medical, dental, and vision coverage, effective from day one.
• Competitive Compensation.
• Family Support: We’re proud to support families of all kinds, and offer generous parental leave, childcare support, and eldercare assistance whenever you need it.
• Wellness Programs: Monthly wellness reimbursement, generous time off, and additional Tubi Holidays help us support mental and physical wellbeing for you and your family.
• Continuing Education: From education reimbursement to leadership development to certification support, we’re invested in developing our talent so you can take your career to the next level.
• Financial Support: We offer resources to help keep you financially fit and invested in your future, from our highly-rated retirement savings matches to financial advisors and planning services.

Experience with deep learning technologies for recommendation systems, including TensorFlow, PyTorch, or similar frameworks8+ years of industry experience building production Machine Learning systemsSolid understanding of statistical concepts such as hypothesis testing, regression analysis, and performance evaluation metrics for machine learningAbility to deep dive into individual components and systems, as well as understand the overall architecture of machine learning solutionsProficiency in building and deploying full-stack machine learning pipelines: data extraction, data mining, model training, feature development, testing, and deploymentMSc or Ph.D. in Computer Science, Machine Learning, Statistics, Mathematics, or a related field