Software Engineer, Machine Learning

Roku•San Jose, CA

3h•Hybrid

About The Position

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.

Requirements

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

Nice To Haves

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

Responsibilities

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