First and most importantly: our mission is to bring transparency and clarity to the world's data. Our platform, FiftyOne, is where AI work happens . Our enterprise platform is the mission critical linchpin for managing unstructured data, model development, and AI systems at the world's largest companies. We believe that open source is the way to lead the data-centric AI revolution. Our open source version has 4 million downloads to-date. Our software massively impacts AI work across almost every vertical: from self-driving cars to medical imaging to revolutionizing agriculture , we are at the thrilling center of real-world AI advancement’s next wave. And we’re built on three key tenets: We are all human beings : we strive to be a “human-first” organization and treat everyone with the respect, care, and flexibility that all people deserve. We are distributed : we believe in getting autonomy and power into the hands of people actually doing the work. We believe in the power of community. We are fully remote, hiring for people based in North America (with a preference for candidates on the West Coast for this role) who are prepared to travel to at least 2 in-person retreats per year, plus travel to various conferences and Meetups. About your role As a Machine Learning Customer Success Engineer at Voxel51, you’ll work directly with our users, helping them identify best practices for their ML workflows using FiftyOne. You’ll partner with teams doing incredible things all over the world - from global-impact Fortune 100 companies to groundbreaking startups - helping them maximize their machine learning capabilities with FiftyOne. Internally, you'll serve as the voice of the customer, a critical role in shaping our product roadmap. You’ll also contribute to a thriving open source ML product and community, and continue to build out our functionality and ecosystem. Every member of our fully-remote team is empowered to own their work and play an active role in advancing our mission to democratize data-centric ML.
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Job Type
Full-time
Career Level
Mid Level