Principal Deep Learning Scientist


About Us

We are a top-tier VC-funded Silicon Valley startup currently in stealth mode. Our company is being built by proven serial entrepreneurs and executives who have scaled multiple successful global companies from the ground-up, and our team includes highly-skilled software engineers and machine-learning experts who have built highly-diverse, custom AI-powered systems in the cloud.

We are changing the game on how AI-powered applications are built and offered to Fortune 5000 global enterprises. We are combining both foundational and cutting-edge advancements in the machine-learning and cloud-systems to highly-researched, high-value products for the enterprise.

Description/What You’ll Do

You will lead the development of one of our deep-learning systems for NLP and NLU. You will play a central and leading role in the development of the deep-learning systems themselves, from neural architecture design to training and deployment. And you will have an opportunity to collaborate with both machine learning and software engineering experts along the way.

Qualifications

  • PhD in computer science or a related technical field with a focus on machine-learning or a related discipline.
  • Proficient developing custom deep neural-network models in PyTorch (preferred), Keras, or TensorFlow
  • Experience deploying production deep natural-language processing models
  • Experience developing reinforcement learning and convolutional neural networks
  • Proficient using established practices to reduce training times, boost accuracy, and reduce overfitting
  • Experience using transfer learning, multi-task learning, or semi-supervised learning to improve efficacy
  • Experience using deep-learning to address core NLP problems such as sentence parsing, parts of speech tagging, named-entity recognition, or coreference resolution
  • Ability to explain and implement a reinforcement learning algorithm (e.g., Q-learning) 
  • Ability to explain and implement 1 or more advanced topics in deep neural networks, such as variational auto-encoders, generative adversarial networks, network visualization, attention networks, or another area
  • Experience using classical NLP machine-learning modeling techniques such as conditional random fields, latent dirichlet allocation, matrix factorization, or similar
  • Very strong understanding of the areas of statistics related to applied predictive modeling

Preferred Qualifications

  • Success independently developing a novel enhancement to a neural architecture, training paradigm, or optimization algorithm to improve training time or efficacy

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