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Senior Machine Learning Engineer - Remote - United States

About the Senior Machine Learning Engineer at Headspace:

Machine Learning at Headspace is a dynamic and innovative group whose mission is to improve the experiences of our members and clinicians through the mindful application of Machine Learning. These applications include building healthcare assistance tools, personalization systems, and NLP/NLU models. In this team, you’ll be tasked with owning and delivering cutting edge ML applications that will power the core features of Headspace. You’ll have the opportunity to lead the vision, alignment, development, deployment, and evangelization of these solutions, helping to bring Headspace to the forefront of AI and to realize its mission to improve health and happiness of the world.

What you will do:

  • Drive impactful ML technology initiatives from inception to production
  • Lead the development of complex, multi-component, scalable AI models and applications
  • Align design and technical decisions across internal organizations
  • Provide technical leadership and be a role model for those pursuing technical career path in AI/ML engineering
  • Promote the development of high-quality code through engagement in code review and constructive feedback
  • Increase internal and external company visibility in the AI community through open source, talks/presentations, etc.

What you will bring:. 

Required Skills:

  • MS or higher in Computer Science or a related field or equivalent experience
  • 3+ years of professional ML engineering experience
  • 2+ years of experience with Natural Language Processing (NLP) & Natural Language Understanding (NLU) systems
  • 2+ years of experience with fundamental AI/ML technologies such as supervised, unsupervised machine learning, deep learning and reinforcement learning
  • Strong experience in OO design and programming in Python 
  • Experience with unit, integration, and end-to-end testing, version control
  • Proven track record of leading cross-team initiatives and driving ML initiatives end-to-end 
  • Strong problem …

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