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Machine Learning Engineer - Manchester, United Kingdom - Roku
Description
Roku is changing how the world watches TVRoku is the #1 TV streaming platform in the US, 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.
We offer you the opportunity to delight millions of TV streamers around the world while gaining meaningful experience across a variety of disciplines.
Roku pioneered TV streaming and continues to innovate and lead the industry. The Roku Channel has us well-positioned to help shape the future of streaming. Continued success relies on building customer relationships with Roku that delight and engages them.The Marketing Engineering team at Roku is focused on building the right products and services that help us provide the right communication to the right customers at the right time.
We are recruiting a Senior Machine Learning Engineer to build and enhance intelligent systems for the marketing team to harness the power of DataOur team's mission is to build cutting-edge advertising technology to support and help grow a sustainable advertising business. The team owns server technologies, data, and cloud services for advertising.
The MarTech team at Roku is looking for a seasoned Senior Machine Learning Engineer with a background in Machine Learning.
Employees in this role are expected to apply knowledge of experimental methodologies, statistics, Reinforcement Learning, Optimisation, Probability Theory, and Machine Learning using code for tool building, statistical analysis, and modelling, using both general-purpose software and statistical languages.
You should be a gritty problem solver and self-starter who can drive programs with the product and commercial teams within Roku and across external strategic partner organisations.
This role requires hybrid working from the Roku, Manchester office.ML-Infrastructure:
Help build a first-class machine learning platform from the ground up which helps manage the entire model lifecycle: feature engineering, model training/evaluation, versioning, deployment/online serving and monitoring prediction quality
Data Analysis/Feature Engineering:
Model training with batch and real-time prediction scenarios: Use machine learning and statistical modeling techniques such as Reinforcement Learning,Decision Trees, Logistic Regression, Bayesian Analysis, Neural Networks and others to develop and evaluate algorithms to improve product/system performance, quality and accuracy
Low level systems debugging, performance measurement & optimization on large production clusters
First-hand experience in applied machine learning on real use cases (brownie points for productionised reinforcement learning use cases)
Great coding skills and strong software development experience (we use Spark, Python and Java a lot)
Can work with large scale computing frameworks, data analysis systems and modeling environments. Bachelors, Masters or PhD program in Computer Science/Statistics or a related field
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.
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.
In short, at Roku you'll be part of a company that's changing how the world watches TV.That penchant for action gives us a pragmatic approach to innovation, one that has served us well since 2002.
