Machine Learning Engineer

Industry: Edtech
Remote
Company size: 1-10
Full-time
Not disclosed

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Job description

An emerging edtech startup building an initial team of talented individuals is looking for a Machine Learning Engineer. The engineer will be actively contributing to developing a computer adaptive testing solution that also employs machine learning for the education space. The company has recently raised a pre-seed fund and aims to implement an Adaptive Learning System. This would be an exciting opportunity for those who want to work in a fast-paced and customer-first environment. It requires an overlap with PST.

 

Job Responsibilities:

  • Assist in building back-end infrastructure, data pipelines, and ML models for AI-backed products
  • Experiment with ML techniques to create and test new program features
  • Assist in extending and improving existing ML frameworks and libraries
  • Work alongside data engineers to create data and model pipelines, and embed AI and analytics into the business decision processes
  • Integrate ML models to end-users and run experiments
  • Run tests, perform statistical analysis, interpret test results, and perform tuning and scaling

Job Requirements:

  • Bachelor’s/Master’s degree in Engineering, Computer Science (or equivalent experience)
  • At least 3+ years of relevant experience as a Machine Learning Engineer
  • Must have hands-on experience in Adaptive Learning Systems 
  • Should be well versed in MEAN and/or MERN stacks
  • Expertise in Machine Learning and Artificial Intelligence 
  • Experience in data science projects and psychometrics is required
  • Must have excellent communication skills with proficiency in English

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In a nutshell, Turing aims to make the world flat for opportunity. Turing is the brainchild of serial A.I. entrepreneurs Jonathan and Vijay, whose previous successfully-acquired AI firm was powered by exceptional remote talent. Also part of Turing’s band of innovators are high-profile investors, such as Facebook's first CTO (Adam D'Angelo), executives from Google, Amazon, Twitter, and Foundation Capital.

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