Software Engineer, Machine Learning
RokuSan Jose, California$135k – $185kPosted 24 February 2026
Job Description
Teamwork makes the stream work.
Roku is changing how the world watches TV
Roku is the #1 TV streaming platform in the U.S., Canada, and Mexico, and we've set our sights on powering every television in the world. Roku pioneered streaming to the TV. Our mission is to be the TV streaming platform that connects the entire TV ecosystem. We connect consumers to the content they love, enable content publishers to build and monetize large audiences, and provide advertisers unique capabilities to engage consumers.
From your first day at Roku, you'll make a valuable - and valued - contribution. We're a fast-growing public company where no one is a bystander. We offer you the opportunity to delight millions of TV streamers around the world while gaining meaningful experience across a variety of disciplines.
About the team
Roku pioneered in developing operating system for TVs. Tv Technology team within Roku TV NPI group is focused on building TVs with best Audio Quality , Picture Quality and Enable innovation technology.
With tens of million TVs sold across many countries, thousands of streaming channels and billions of hours watched over the Roku TVs, having best Audio Quality Picture Quality is crucial.
About the role
We are looking for a creative and passionate Machine Learning (ML) / Deep Learning (DL) Engineer to join the Roku TV team. You will explore Picture Quality (PQ) and Audio Quality (AQ) improvements using AI in a resource-constrained environment, pushing the limits of what is possible in TV technology. Additionally, this role will involve applying state-of-the-art ML techniques to different PQ and AQ related tasks. You will play a critical role in building scalable systems and algorithms that enhance streaming experiences for millions of users worldwide.
For California Only - The estimated annual salary for this position is between $135,000 - $185,000 annually. Compensation packages are based on factors unique to each candidate, including but not limited to skill set, certifications, and specific geographical location. This role is eligible for health insurance, equity awards, life insurance, disability benefits, parental leave, wellness benefits, and paid time off.
What you’ll be doing
Explore and implement AI-driven Picture Quality and Audio Quality improvements for Roku TV
Apply advanced ML techniques in the optimization of models to enhance video and audio processing pipelines
Research, understand, and implement technical papers on ML/DL to solve complex challenges
Collaborate with engineering teams and cross-functional partners to translate business requirements into technical solutions
Run experiments and analyze results against critical business KPIs
We’re Excited If You Have
1+ years of experience applying Machine Learning to concrete problems at scale
Strong CS fundamentals; ability to write efficient algorithms with ease
Hands-on experience with Machine Learning / Deep Learning frameworks like TensorFlow or PyTorch
Proficiency in coding firmware development using C++
Proficiency in coding ML/DL algorithms using Python
Solid understanding of DL fundamentals, including RNN, CNN, and multi-modal models
Familiarity with video processing pipelines and audio pipelines
AI literacy and curiosity—whether through previous work or personal exploration
Excellent communication skills, strong work ethic, and works with minimum supervision
Have hands-on experience with edge devices like jetson nano, kadas, and similar
Research background preferred; ability to understand and implement technical papers on ML/DL
Published papers in the field of ML/DL are a bonus
Hold an MS or PhD in Computer Science, Electrical Engineering, or related fields - Preferred
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Our 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 are required to be in the office five ... (truncated, view full listing at source)
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