Head of Machine Learning Engineering

Espressive Inc. in Santa Clara, CA seeks Head of Machine Learning Engineering. Lead and provide architectural inputs to teams working together to build and deliver features. Design and build compelling features that leverage AI/ML services and enable enterprises to provide exceptional experience to the employees in their day-to-day function. Design and build integrations that allow enterprises to seamlessly extend Espressive services with third party applications. Create, maintain, and document secure REST APIs to meet product requirements. Optimize code and data to ensure scalability and performance. Write unit tests against newly created components to keep code coverage up. Work across AI/ML, Platform, and Operations teams to solve complex challenges together. Collaborate with the frontend team to ensure APIs meet requirements for delivering a robust user experience. Collaborate with the quality assurance team to validate and resolve defects. Work with the product/UX team to address challenges in a production application. Mentor junior engineers and interns. Maintain the confidentiality, non-competition, non-solicitation, and Intellectual Property Assignment Agreement. Follow the Handbook and code of conduct conditions. 100% telecommuting from home allowed from anywhere in the United States. Salary: $283,442.00/year.

Must possess a Master's Degree in Computer Science, Statistics, Mathematics, or a related quantitative field. Must have 3 years of experience in the job offered or a related field. Must also possess experience with: (i) Python and database skills; (ii) GO; (iii) Frameworks such as Django; (iv) Fundamental design principles behind a scalable cloud application; (v) Integrating multiple data sources into one system; (vi) Building enterprise cloud-based applications; (vii) Web sockets; (viii) Message Queues; (ix) Caching; (x) PubSub; (xi) NoSql; (xii) User authentication and authorization between multiple systems, servers, and environments; (xiii) Natural Language Generation (NLG); (xiv) Natural Language Understanding (NLU); (xv) Natural Language Processing (NLP); (xvi) Algorithms in Multilingual Conversational Systems for intent, entity detections, and automated conversations; (xvii) Machine learning frameworks such as TensorFlow and PyTorch; (xviii) Container technologies such as Docker; (xix) Debugging; and (xx) Cloud product security.