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    Senior Machine Learning Engineer - Manchester, United Kingdom - Roku

    Roku
    Roku Manchester, United Kingdom

    Found in: Talent UK C2 - 1 day ago

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    Description

    Teamwork makes the stream work.

    Roku is changing how the world watches TV

    Roku 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.

    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 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 Data

    Our 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.

    About the role

    The MarTech team at Roku is looking for a seasoned Senior Machine Learning Engineer with a background in Machine Learning. Examples of such problems include determining the next best action for customers, improving ad relevance, inferring demographics, yield optimisation, and many more. 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.

    The ideal candidate will have endless curiosity and can pair a global mindset with locally relevant execution. 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. The successful candidate will display a balance of hard and soft skills, including the ability to respond quickly to changing business needs.

    This role requires hybrid working from the Roku, Manchester office.

    What you'll be doing

  • 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: Apply your expertise to identify and calculate features that can be leveraged by multiple use cases as well as models
  • 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
  • We're excited if you have

  • First-hand experience in applied machine learning on real use cases (brownie points for productionised reinforcement learning use cases)
  • Experience with ML/distributed ML frameworks like Spark-MLlib, TensorFlow etc.
  • Experience with real-time scoring/evaluation of models with low latency constraints
  • 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. Examples include technologies like Spark, Hive, NoSQL stores etc.
  • Bachelors, Masters or PhD program in Computer Science/Statistics or a related field
  • Ad-tech background is a plus
  • #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.


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