Deep-Learning Engineer

2 weeks ago

New York, United States PSYKHE AI Full time

At PSYKHE AI, we use deep learning, and psychographic attribution, to deliver a next-generation personalization solution for ecommerce. We serve both our retail clients and their end-consumers. This is a contract role with the potential to develop into permanent with equity and benefits for the right mutual fit.



The Problem We’re Solving


When it comes to machine learning, our product differentiates in a few ways: 


  • We use real-time user data. That’s important both when a recurring user comes back with a different intent, or when it’s the first time a user visits a site.


  • We think good personalization comes from a deep understanding of each product. We use all data points and available, and psychographics, to build a complete picture of the link between a product and the user.


  • Finally, our solution has to work for a diverse group of retailers (i.e. diverse catalog size, catalog diversity, traffic, audience) and therefore our models need to adapt to very different data regimes.


The Role 


At PSYKHE AI, your key responsibilities will span the entire lifecycle of our deep-learning models, and you'll be exposed to all aspects of the ML/DL learning lifecycle.


Key Responsibilities:


  • ML/DL engineering as blow
  • Improve our feature-extraction pipeline to build deep insights into our retail clients’ products.
  • Extend our existing recommendation model training codebase.
  • Extend our evaluation processes to be able to predict how well a model will perform in production early in its lifecycle.
  • ML/DL research: Experiment with new models for recommendation based on insights from our live performance. Be scientifically minded to increase our understanding of which models and parameters work best in different data regimes by running experiments.
  • ML ops: 
  • Work and coordinate with the engineering team when releasing improvements to our production system
  • Improve our tooling to allow for quick deployment and a quick feedback loop
  • Models maintenance: Deploy models on our production system and monitor their performance


About You


Required qualifications: 

  • Bachelors or masters in computer science, mathematics or physics
  • 3+ years of industry experience putting machine-learning models in production
  • Good understanding deep-learning framework (pytorch, tensorflow, or other)
  • Experience training deep neural networks and putting them into production


Preferred qualifications: 

  • Masters in computer science, mathematics or physics
  • Exposure to the entire lifecycle of machine learning models from experimentation to production (MLops)
  • Experience contributing to data-engineering pipelines
  • Experience building machine-learning models for recommendation or personalization
  • Some experience with NLP and computer vision


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