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