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\nThis role is US Remote/San Francisco based.\n\n ABOUT US\n\nAt Rasa, we're building the standard infrastructure for conversational AI. With over half a million downloads since launch, our open source tools are loved by developers worldwide, and Rasa runs in production everywhere from startups to Fortune 500s. Our friendly community is growing fast, with developers from all over the world learning from each other and working together to make text- and voice-based AI assistants better.\n\nRasa's machine learning-based dialogue tools allow developers to automate contextual conversations. What are contextual conversations? Real back-and-forth dialogue that is handled seamlessly. Taking AI assistants beyond fixed question / answer pairs creates exciting new use cases for people and business. The tip of the iceberg includes automation of sales & marketing, internal processes, and advanced customer service that integrates into existing backend systems. With Rasa, companies control their own destiny, investing in AI that they own and ship instead of relying on third parties.\n\nRasa has raised $14 million in total funding from Accel, Basis Set Ventures and open source founders such as Ross Mason (MuleSoft), Mitchell Hashimoto (Hashicorp) and Florian Leibert (Mesosphere). The company is headquartered in San Francisco, CA, with R&D offices in Berlin, Germany and was founded in 2016.\n\nRasa is an equal opportunity employer. We are still a small team and are committed to growing in an inclusive manner. We want to augment our team with talented, compassionate people irrespective of race, color, religion, national origin, sex, physical or mental disability, or age.\n\nSUMMARY \n\nWe are looking for enthusiastic support engineers to help our customers with the use of our product, from debugging machine learning models to debugging their docker setup. Doing this well is core to the success of the company.\n\nABOUT THIS ROLE\n\nThousands of developers worldwide build voice and chat systems with Rasa. As a solutions architect, you will be working directly with developers and product managers at companies using Rasa to build conversational assistants. You’ll support them in development, building models, testing and resolving issues. \n\nYou will collaborate closely with Rasa’s product engineers to improve our product, including API design, docs, and usability. \n\nPlease keep in mind that we are describing the background we imagine would best fit the role. Even if you don’t meet all the requirements, yet you are confident that you are up for the task, we absolutely want to get to know you!\n\nABOUT YOU \n\nYou are excited about conversational software and letting people interact with machines through text and speech. You have experience programming in a couple of languages and a good understanding of the machine learning basics. You’re good at finding the root cause of a bug, and can find a solution or workaround when the obvious fixes haven’t worked.\n\nYou want to gain more experience with natural language processing, applied machine learning, and putting AI systems into production.\n\nRequirements:\n\n\n* \n\nDegree in computer science or a related field, or at least 2 years experience developing software.\n\n\n* \n\nFamiliarity with machine learning concepts\n\n\n* \n\nExperience teaching & communicating technical material\n\n\n* \n\nPractical experience applying machine learning\n\n\n* \n\nExperience supporting customers in a technical role\n\n\n* \n\nComfortable with most of the following: linux, python, docker, kubernetes \n\n\n\n\n\nNice to have:\n\n\n* \n\nExperience applying NLP\n\n\n* \n\nExperience shipping chatbots or voice apps\n\n\n\n\n\nTHINGS YOU WILL DO \n\nWe’re a startup, so you’ll have to be comfortable rolling up your sleeves and doing whatever is required to support our mission. However, you can definitely expect to:\n\n\n* \n\nHelp our customer's engineers build ML-based bots and assistants with Rasa\n\n\n* \n\nHelp them debug their installations of our product\n\n\n* \n\nBe the voice of our customers in product discussions, using what you’ve learned from helping them succeed to make our products more usable and valuable\n\n\n* \n\nReport back when a customer encounters shortcomings in our products and discuss how to improve them with our product and applied research teams\n\n\n* \n\nRun a workshop on best practices with the Rasa stack\n\n\n* \n\nWrite a blog post explaining some aspect of Rasa’s code in detail\n\n\n* \n\nCollaborate with product teams to make our open source libraries easier to use\n\n\n\n
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