Senior Machine Learning Engineer

RokuSan Jose, CA
$148,750 - $361,000Hybrid

About The Position

The Advertising Performance group focuses on performance for all participants in the Advertising ecosystem - Advertisers, Publishers and Roku. The systems and solutions span across different disciplines and technologies to perform realtime multi-objective optimization with distributed systems at large scale and low latencies. We use Machine Learning, Reinforcement Learning, AI, Control and Optimization Systems and Auction Dynamics to solve a large set of complex problems. At the core of this is our Machine Learning, Experimentation and Inference Platform that powers the entire landscape which we continuously evolve over time. In this role you will apply various methodologies to solve a large variety of challenging problems in Advertising related to conversion modeling aligned with attribution methodologies/models, calibration, dynamic creative generation and optimization, forecasting and timeseries modeling, yield and margin optimization and Experimentation for A/B and multivariate testing. You will also work on building out a SOTA machine learning platform. We’re looking for strong engineers well versed with modern large scale machine learning platforms with a solid grasp of core statistical techniques and deep experience in SOTA Deep Learning discriminative and generative models.

Requirements

  • BS or higher in CS, ECE or a related field
  • 5+ years of experience in building out Machine Learning platforms or applying Deep Learning methodologies
  • Ability to communicate and collaborate cross functionally

Nice To Haves

  • MS or higher CS, ECE or a related field
  • Experience in the Advertising domain
  • Contributions to open-source ML projects

Responsibilities

  • Building SOTA Deep learning discriminative models and build generative models to generate image and video ads geared towards optimizing performance
  • Building and evolving a SOTA Machine Learning Platform from feature generation to realtime inferencing that can optimize and deploy complex models at large scale and low latencies
  • Be forward thinking about advancements in ML related areas

Benefits

  • global access to mental health and financial wellness support and resources
  • healthcare (medical, dental, and vision)
  • life, accident, disability, commuter, and retirement options (401(k)/pension)
  • paid time off
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