Senior Applied Machine Learning Engineer

About the Role

 

As a Senior Applied Machine Learning Engineer, you are willing to contribute wherever needed to get our products out the door. Any company growing at scale thrives on its ability to continuously innovate on new products and features. You will be hands-on and responsible for the full model development life cycle and the full software development life cycle of cloud backend software. In this role, you will contribute extensively to our machine learning infrastructure, cloud architecture, platform integrations, code maintenance, QA efforts and everything else involved in our end-to-end product development and release cycles. 

What you’ll be doing 

Deploying scalable, fault tolerant computer vision and machine learning systems to production  Overseeing the full model development cycle: ideation, prototyping, implementation, deployment, testing, and operations Designing uncertainty metrics and communicating results to a mix of technical and non-technical stakeholders Gathering/compiling datasets, defining annotation ontologies, auditing the work of annotation operations, and taking responsibility for data quality Staying up to date with industry/academic trends in computer vision and machine learning  Working closely with product and other engineering teams to implement new experiences and cloud services Integrating services from other teams around the company, while also providing services to other teams  Evaluate and provide feedback on new platform technologies provided by other teams Working with QA teams to address bugs and aid in automation of software as needed

We’re Excited If You Have 

Masters degree (PhD preferred) in the area of computer science or related field At least five years of experience developing production applied machine learning systems using frameworks such as PyTorch or Tensorflow At least two years of research or equivalent industry experience with state-of-the-art Image Processing, Computer Vision, or Natural Language Processing Comfortable using cloud services for storing data, training and serving models from providers such as AWS, GCP, or Azure Experience evaluating models and communicating findings Experience building APIs with frameworks such as GraphQL or REST Experience with workflow orchestration tools such as Airflow, Argo, AWS Step Functions, or Metaflow We're especially excited if you have experience with Docker / Kubernetes. IaC tools (Terraform, CloudFormation), Model Serving, C++, CUDA, CI/CD Automation, Python build/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.