Senior Machine Learning Scientist, Conversion Modeling

Roku; San Jose (CA); Hybrid; USD 148,750 - 361,000; Full time

Benefits offered by this job

Health insurance

Equity awards

Life insurance

Disability benefits

Parental leave

Wellness benefits

Paid time off

Job summary

Roku seeks a qualified candidate to develop innovative methodologies addressing complex advertising challenges. This role involves building generative models for optimizing ad performance through cutting-edge research.

The ideal applicant possesses a PhD in a quantitative discipline, with over 8 years of experience and relevant publications in deep learning. The position supports a hybrid work approach and offers extensive benefits including health insurance, equity awards, and more.

Qualifications

• 8+ years of experience in applied research using statistical and deep learning techniques.
• Published paper(s) on deep learning models for advertising or related areas.

Responsibilities

• Building generative models to generate image and video ads geared towards optimizing performance.
• Stay at the forefront of advancements in related areas.

Skills

Statistical and deep learning techniques

Excellent communication and collaboration skills

Education

PhD in Computer Science, Statistics, Applied Mathematics or related field

Job description

Company Overview

Roku is the #1 TV streaming platform in the United States, Canada, and Mexico. Our mission is to power every television in the world, connecting consumers to the content they love and enabling publishers and advertisers to reach large audiences.

Team

The Advertising Performance group focuses on performance for all participants in the advertising ecosystem. We build systems and solutions that perform real‑time multi‑objective optimization, leveraging machine learning, reinforcement learning, AI, control and optimization systems, and auction dynamics.

Role Overview

In this role you will apply state‑of‑the‑art research and develop novel methodologies to solve a variety of complex problems in advertising, including conversion modeling, attribution, calibration, dynamic creative generation and optimization, forecasting and time‑series modeling, yield and margin optimization, and experimentation for A/B and multivariate testing.

Responsibilities

• Building generative models to generate image and video ads geared towards optimizing performance
• Stay at the forefront of advancements in related areas

Qualifications

• PhD in a quantitative discipline such as Computer Science, Statistics, Applied Mathematics or a related field
• 8+ years of experience in applied research using statistical and deep learning techniques
• Published paper(s) on deep learning models for advertising or related areas
• Excellent communication and collaboration skills

Preferred Qualifications

• Experience in the advertising domain
• Contributions to open‑source machine learning projects

Salary and Location

For California only, the estimated annual salary for this position is between $148,750 and $361,000. Compensation packages are based on factors unique to each candidate, including skill set, certifications, and geographical location. The role offers health insurance, equity awards, life insurance, disability benefits, parental leave, wellness benefits, and paid time off.

Hybrid Work Approach

Roku fosters an inclusive and collaborative environment where teams work in the office Monday through Thursday. Fridays are flexible for remote work, except for employees whose roles require a five‑day in‑office presence.

Benefits

Roku offers a diverse range of benefits, including global mental health and financial wellness support, statutory and voluntary benefits such as healthcare (medical, dental, vision), life insurance, accident and disability insurance, commuter benefits, and retirement options (401(k)/pension). Local benefits may vary by location.

Accommodations

Roku welcomes applicants of all backgrounds and provides reasonable accommodations in accordance with applicable law. If you require accommodation during the hiring process, please contact [e-mail].