Associate Machine Learning Engineer

About the Role: 

The Tubi Builder’s Program is designed for early-career professionals in Product, Engineering, Machine Learning, Design and Data Science who are passionate about building products that impact millions of users. Over the course of the program, participants will rotate through different machine learning teams - such as user recommendations and ranking, content understanding, and search ranking - to develop a well-rounded technical foundation. Builders gain hands-on experience across multiple areas of machine learning engineering teams in a fast-paced tech environment.

As a associate machine learning engineer, you will contribute to real-world projects, solve complex technical challenges, and collaborate with experienced engineers, product managers, and data scientists.

This program is ideal for candidates who are curious, adaptable, and excited to work across multiple machine learning and artificial intelligence domains while developing expertise in cutting-edge technology.

What You'll Do:

Throughout the program, you will:

• Rotate across three machine learning engineering teams, gaining exposure to different technical disciplines
• Build and deploy high-impact robust ML pipelines, including data extraction, feature development, model training, testing, and deployment
• Ship production ML systems and features that impact millions of real users
• Leverage cutting-edge AI technologies to build technical solutions at scale
• Define success metrics and evaluate impact via offline and online experiments
• Monitor, evaluate, and optimize the performance of deployed models
• 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
• Participate in mentorship, technical training, and networking events throughout the program
• Engage in engineering-wide initiatives to improve processes and technical standards

Qualifications: 

Minimum Requirements:

• Master’s or PhD degree in Computer Science or a related field with emphasis on Machine Learning
• Strong coding proficiency in at least one programming language (e.g., Python, Java, C++, Go)
• Solid understanding of computer science fundamentals, algorithms, and system design
• Demonstrated understanding and interest in machine learning engineering
• Passion for problem-solving, collaboration, and building scalable systems
• Strong communication skills and ability to work in a team-oriented environment

Preferred Qualifications:

• Work Experience: Up to three years of combined professional experience, including internships
• Professional experience (e.g. internship, research assistantship or work experience) in some software engineering areas (infrastructure, machine learning, or front-end development)
• Familiarity with cloud platforms (AWS, GCP, Azure) and DevOps tools.
• Experience with machine learning
• Demonstrated curiosity and interest in an engineering area through research, past jobs, open-source contributions, or side projects

Program Eligibility Requirements:

• GPA Requirement: Minimum 3.0 GPA
• Application Deadline: 2/15/2026
• Program Timeline: Minimum 18 month commitment, starting Aug 2026, after which successful participants will have the opportunity to transition into non-rotational full-time roles
• Work Schedule: Full-time, hybrid in San Francisco with office day requirements
• Work Authorization: Must have U.S. work authorization; we are unable to sponsor visas for this program.
• Committed and available to work for the entire 18 month length of the program

About the Program:

Our Machine Learning Engineering Rotational Program is structured to accelerate the careers of early-stage engineers by providing them with hands-on experience in multiple technical domains. Through structured rotations, mentorship, and targeted development, participants will gain technical depth and breadth, preparing them for a non-rotational engineering role.

By the end of the program, you will:

• Have experience across multiple machine learning engineering disciplines.
• Develop technical and leadership skills.
• Build a strong professional network within the organization.

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