Principal Applied Research Machine Learning Engineer

2 days ago

London, United Kingdom PC Games Insider Full time
Principal Applied Research Machine Learning Engineer

At Unity, our Inclusion is driven by one overarching framework: Empathy, Respect, and Opportunity. In a collaborative, fast-growing environment, we are solving hard problems and enabling the success of our community. At Unity, you will make a difference. In the Unity Ads Applied Research team, we envision and build systems that help creators capture the value they build

Come join Unity's Ads Applied Research team to help drive the overall Ads strategy for Unity's products and services. In this position, you'll be driving our applied research and analysis on how to improve the optimization and machine learning powering our ad networks, marketplace, and audience tools.

RESPONSIBILITIES

  1. As a machine learning and data science expert, you will guide and drive the application of machine learning solutions across the whole organization. Your role will be to:
  2. Define and execute advanced machine learning models and algorithms to help translate complex business problems.
  3. Be at the forefront of the latest machine learning research to improve Unity’s Ad platform Machine Learning capabilities.
  4. Optimize, improve, and innovate ML practices to establish Unity as an industry leader.
  5. Form and validate hypotheses on new aspects of modeling and the potency of new datasets for ML.
  6. Drive optimization hypotheses by analyzing the drivers for impactful machine learning and arguing the hypotheses and success for the next steps of development.
  7. Communicate data and machine learning capability needs to the product and engineering teams as the authority in Data for ML.
  8. Drive the overall direction of the machine learning future for Ads to support strategic goals and vision.

REQUIREMENTS

  1. We are looking for a candidate with experience in guiding machine learning development via revolutionary steps to keep Unity Ads at the forefront of ML performance.
  2. PhD in Computer Science, Mathematics, Statistics, or a related technical field, or equivalent experience in interpreting and translating research to applications.
  3. Experience successfully taking research and turning it into concrete product innovations with impact.
  4. Expertise with modeling frameworks (e.g., TensorFlow, PyTorch) and understanding of model architectures and mechanisms.
  5. Ability to explain machine learning and interpret the modeling efficacy for further improvements.
  6. Capable of conducting analysis, statistics, and data literacy to deduce where the challenges and opportunities lie.
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