Here is a summary of the kind of work you will be tasked with: 1. Machine Learning a. Build an ongoing understanding of current state-of-the-art NLU architectures, methods, and processes, and innovate networks that create actionable intelligence from text. b. Understand different media types (images, audio, video, etc.) and build networks that extract information and meaning from these media. c. Create networks that curate (and/or generate) media (text today, everything else tomorrow). Understand the problems that go into building these networks and innovate solutions to mitigate these problems. d. Understand patterns in time-series (and other) data, and create networks that evolve as these patterns do, using SOTA reinforcement learning techniques, Bayesian networks, and other methods. e. Build search and recommendation systems based not just on media but on context and meaning. 2. Data Science a. Build a deep understanding of usage patterns and patterns in language. b. Provide statistical input and solutions to help product teams make better decisions. c. Help understand, clean, and generate datasets for networks. Help interpret network outputs and make them comprehensible to people outside the team. d. Report on customer trends and deployment performance, and identify areas that we can target using ML/Data Science solutions. 3. Engineering: a. This role requires a thorough understanding of core computer engineering and computer science and is not limited to programming, database administration, or engineering the cloud. You will be called on to do all of these. For us, full-stack really means full-stack. b. Define and help build the APIs our various products use to deliver ML-based solutions to customers. c. Understand and decimate engineering challenges that stand in the way of optimal speeds and reliability across all our deployments. d. A deep understanding of algorithms and first principles in computing is helpful for this area of focus.