We are 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.
Even in stealth, 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 work with a team of machine-learning experts and seasoned software engineers in the ground-up development of our deep-learning systems. Such systems will be used to dramatically improve the efficacy of our core NLP and NLU systems as well as to improve the speed of several critical optimization systems through the use of GPU-based, highly-parallelized neural-network computation models. You will play a central role in the development of the deep-learning models themselves, collaborating on neural architecture designs, resource needs, training data requirements, and, ultimately, driving training and deployment.
- Masters or PhD in computer science or a related technical field with a focus on statistics, optimization, or machine-learning.
- Proficient developing custom deep neural-network models in PyTorch (preferred), Keras, or TensorFlow
- Experience using established practices to reduce training times, boost accuracy, and reduce overfitting
- Experience using deep-learning to address core NLP problems such as sentence parsing, parts of speech tagging, named-entity recognition, or coreference resolution
- Experience building both CNN and RNN (e.g., LSTM) models for NLP tasks
- Experience using transfer learning, multi-task learning, or semi-supervised learning to improve efficacy
- Ability to explain and implement a reinforcement learning algorithm (e.g., Q-learning)
- Ability to explain and implement an advanced topic 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
- Experience deploying production deep natural-language processing models
- Successful experience building models using reinforcement learning
- Success independently developing a novel enhancement to a neural architecture, training paradigm, or optimization algorithm to improve training time or efficacy