Senior Machine Learning Engineer

Roku•San Jose, CA

14d•$229,500 - $360,000•Hybrid

About The Position

The Recommendations team drives personalized experiences across our platform by leveraging state-of-the-art machine learning. Our mission is to deliver meaningful, context-aware recommendations that adapt to each user's preferences in real time. We believe that true innovation in personalization requires more than great models—it depends on a robust, flexible ML platform built for experimentation and scale. To that end, we design and build the underlying ML infrastructure, ensuring our systems remain fast, reliable, and at the forefront of technology. Our work blends innovation, engineering excellence, and a deep commitment to understanding our users, shaping how they discover and engage with content every day. We seek an outstanding, creative, and passionate Machine Learning Platform Engineer to join Roku's Recommendation team. In this role, you will design, build, and scale robust distributed systems that power the next generation of personalized content recommendations for millions of Roku users. You will focus on developing end-to-end machine learning platforms and infrastructure, ensuring seamless deployment, monitoring, and optimization of algorithms and operational workflows that deliver unique experiences at scale.

Requirements

5+ years of experience building software solutions to concrete problems
Strong CS fundamentals. Should be able to write an algorithm with ease
Fluent with one of the high-level programming languages like Java, Scala, Kotlin, or Python
MS in Computer Science or related field

Nice To Haves

Worked with big data systems (Spark, Kafka, Flink, S3, AirFlow)
Familiar with model ML framework and tools: Ray, PyTorch, HuggingFace, AWS Sagemaker
AI literacy and curiosity. You have either tried Gen AI in your previous work or outside of work, or are curious about Gen AI and have explored it.

Responsibilities

Design, build, and maintain scalable platform services: feature store, real-time inference services, vector DBs, etc., that serve millions of transactions per second
Run and monitor online AB tests via robust platform services, analyzing platform metrics and business KPIs to optimize recommendation system performance
Collaborate closely with US-based engineering and cross-functional teams to translate business requirements into modular platform components and APIs
Enhance and evolve the ML platform ecosystem to support high developer velocity, system scalability, and adaptability to future business needs
Contribute to onboarding, training, and mentoring new team members on emerging platform engineering best practices and technologies