Senior Machine Learning Engineer (Applied ML / Generative AI)

About the Role

As a Senior Applied Machine Learning Engineer, you’ll help design, build, and deploy the systems that make media smarter.
You’ll work across the full model and software lifecycle, from prototype to production, developing scalable ML pipelines and cloud architectures that power generative AI, intelligent media understanding, content analysis, and advertising intelligence.

You’ll operate at the intersection of machine learning, infrastructure, and software engineering, taking ownership from data collection through deployment — and seeing your work directly influence how audiences experience Roku’s content and advertising ecosystem.

What You’ll Be Doing

Deploying scalable, fault-tolerant computer vision, media understanding, and generative AI systems to production

Overseeing the full model development cycle: ideation, prototyping, implementation, deployment, testing, and operations

Designing uncertainty metrics and communicating results to both technical and non-technical stakeholders

Gathering and compiling datasets, defining annotation ontologies, auditing annotation operations, and ensuring data quality

Staying up to date with industry and academic trends in computer vision, machine learning, and generative models for media and advertising

Working closely with product and other engineering teams to implement new content and advertising experiences through cloud services

Integrating services from other teams around the company, while also providing reusable ML services to others

Evaluating and providing feedback on new platform technologies provided by internal teams

Working with QA teams to address bugs and contribute to automation and quality assurance

We’re Excited If You Have

A Master’s degree (PhD preferred) in Computer Science, Applied Mathematics, or a related field

Strong background developing applied machine learning systems using PyTorch or TensorFlow

Expertise in image processing, computer vision, or natural language processing

Experience using AWS, GCP, or Azure for storing data, training, and serving models

Proven ability to evaluate models and communicate insights effectively

Experience building APIs with frameworks such as GraphQL or REST

Experience with workflow orchestration tools such as Airflow, Argo, AWS Step Functions, or Metaflow

Hands-on experience with Docker, Kubernetes, Terraform, CloudFormation, CI/CD automation, and Python build or packaging tools

#LI-PA1

Benefits

Roku is committed to offering a diverse range of benefits as part of our compensation package to support our employees and their families. Our comprehensive benefits include global access to mental health and financial wellness support and resources. Local benefits include statutory and voluntary benefits which may include healthcare (medical, dental, and vision), life, accident, disability, commuter, and retirement options (401(k)/pension). Our employees can take time off work for vacation and other personal reasons to balance their evolving work and life needs. It's important to note that not every benefit is available in all locations or for every role. For details specific to your location, please consult with your recruiter.

The Roku Culture

Roku is a great place for people who want to work in a fast-paced environment where everyone is focused on the company's success rather than their own. We try to surround ourselves with people who are great at their jobs, who are easy to work with, and who keep their egos in check. We appreciate a sense of humor. We believe a fewer number of very talented folks can do more for less cost than a larger number of less talented teams. We're independent thinkers with big ideas who act boldly, move fast and accomplish extraordinary things through collaboration and trust. In short, at Roku you'll be part of a company that's changing how the world watches TV. 

We have a unique culture that we are proud of. We think of ourselves primarily as problem-solvers, which itself is a two-part idea. We come up with the solution, but the solution isn't real until it is built and delivered to the customer. That penchant for action gives us a pragmatic approach to innovation, one that has served us well since 2002.