Senior Machine Learning Engineer - AdTech in Manchester

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 is the No. 1 TV streaming platform in the U.S., Canada, and Mexico with 70+ million active accounts. Roku pioneered streaming to the TV and continues to innovate and lead the industry. We believe Roku’s continued success relies on its investment in our machine learning/ML recommendation engine. Roku enables our users to access millions of contents including movies, episodes, news, sports, music and channels from all around the world.

About the role: We’re on a mission to build cutting-edge advertising technology that empowers businesses to run sustainable and highly-profitable campaigns. The Ad Performance team owns server technologies, data, and cloud services aimed at improving the ad experience. We're looking for seasoned engineers with a background in machine learning to aid in this mission. Examples of problems include improving ad relevance, inferring demographics, yield optimisation, and many more. Employees in this role are expected to apply knowledge of experimental methodologies, statistics, optimisation, probability theory, and machine learning using both general purpose software and statistical languages.

What you’ll be doing:

• ML infrastructure: Help build a first-class machine learning platform from the ground up which manages the entire model lifecycle - feature engineering, model training, versioning, deployment, online serving/evaluation, and monitoring prediction quality.
• Data analysis and feature engineering: Apply your expertise to identify and generate features that can be leveraged by multiple use cases and models.
• Model training with batch and real-time prediction scenarios: Use machine learning and statistical modelling techniques such as Decision Trees, Logistic Regression, Neural Networks, Bayesian Analysis and others to develop and evaluate algorithms for improving product/system performance, quality, and accuracy.
• Production operations: Low-level systems debugging, performance measurement, and optimisation on large production clusters.
• Collaboration with cross-functional teams: Partner with product managers, data scientists, and other engineers to deliver impactful solutions.
• Staying ahead of the curve: Continuously learn and adapt to emerging technologies and industry trends.

We’re excited if you have:

• Bachelors, Masters, or PhD in Computer Science, Statistics, or a related field.
• Demonstrated depth in applied machine learning on production systems — typically 6+ years in industry, although we value PhD experience as meaningful acceleration.
• Great coding skills and strong software development experience (we use Spark, Python, Java).
• Familiarity with real-time evaluation of models with low latency constraints.
• Familiarity with distributed ML frameworks such as Spark-MLlib, TensorFlow, etc.
• Ability to work with large scale computing frameworks, data analysis systems, and modelling environments. Examples include Spark, Hive, NoSQL stores such as Aerospike and ScyllaDB.
• Ad tech background is a plus.

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 days a week or employees who are in offices with a five day in office policy.

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). Employees are supported in taking time off, in accordance with local leave policies and other personal needs to support 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.

Accommodations: Roku welcomes applicants of all backgrounds and provides reasonable accommodations and adjustments in accordance with applicable law. If you require reasonable accommodation at any point in the hiring process, please direct your inquiries to EmployeeRelations@Roku.com.

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.