Our client is an AI/ML technology start-up that is based on the science and technology of Artificial Intelligence. The founders have a wealth of experience working on various ground-breaking products including self-driving cars, AWS AI services, GMail, Google Docs and flash storage systems. Backgrounds include key roles at Google, AWS, Uber, founding team of a startup that had a billion-dollar IPO, and degrees from IIT, Stanford, and Dartmouth.

They are looking for a Vice President of Engineering to lead a talented team of software engineers, machine learning software engineers, and research scientists as part of the founding team. You will help build the product that applies unsupervised learning to model the world and automatically creates and manages production-grade AI systems.

As an initial member of the founding team, you will own a significant chunk of the company, shape its culture and work on state-of-the-art science and technology.

Job Description:

• Responsible for coming up with new techniques in unsupervised learning, dataset augmentation, and deep reinforcement learning that can be applied to automating various parts of the AI development workflow.
• Responsible for implementing various algorithms to do automated feature extraction and dataset augmentation, optimizing runtimes of neural network algorithms, and building higher-level abstractions for various common AI/ML techniques.
• Responsible for building out a cloud runtime for an AI engine that automates various aspects of an ML/AI system workflow including feature pipelines, model training, and a real-time multi-tenant inference system.

Technology Experience:

• Bachelors, Masters or Ph.D. from Top-Notch University; degree in Computer Science, Artificial Intelligence, Machine Learning or related technology disciplines  
• 10+ years of professional SDLC experience
• At least 5 years with Machine Learning, Data Science, Deep Learning, Neural Networks, and related technologies
• At least 5 years in a Senior Management Level position within a successful start-up organization
• Experienced at growing a team of machine learning and software engineers - who are experienced in building data platforms
• ML/AI models which are in production
• New neural network algorithms based on research papers
• Low-level performance optimization of deep learning systems
• Machine learning platforms
• Large scale backend systems used by consumer or high volume enterprise services
• Cloud data processing platforms in production
• Large scale machine learning pipeline infrastructure
• Experience building production applications that use ML/AI
• Unsupervised Learning
• Generative Modeling
• Deep Neural Networks
• Deep Reinforcement Learning
• Generative Adversarial Networks
• Causal Reasoning