Unwrap

Applied NLP Engineer

Santa Barbara, California, United StatesFull time$160,000 - $220,000 / yearPosted 14 days ago
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Do you love to build? Are you one of the most ambitious people you know? If so, you'll be right at home at Unwrap.

We are seeking an Applied NLP Engineer who has a specific interest in building customer-facing technology for extracting actionable insights from large scale customer feedback. Our customers are some of the world's best companies, from Github to Stripe to Oura to Microsoft. These teams rely on Unwrap to analyze millions of pieces of customer feedback and inform them where they need to focus.

You’ll own and deliver major customer-facing solutions end-to-end. This includes understanding customer needs, helping compile sprint requirements, and ultimately shipping core NLP features. You’ll get exposure to all aspects of the company, from sales to marketing to customer success, but you’d focus primarily on designing and building NLP features that drive the product and the insights we deliver forward.

This is an in-person role in beautiful Santa Barbara, CA. Our office is downtown, walking distance to great restaurants, coffee shops, and the beach.

Who We Are

Unwrap.ai http://Unwrap.ai is on a mission to fill the world with products people love. We’re helping companies like Lyft, Stripe, Oura, Microsoft, Perplexity, and Github collect and process feedback more effectively. We ingest feedback from thousands of sources (support channels, surveys, social), and use state-of-the-art NLP technology to extract actionable insights for customers across software, hardware, and retail sectors.

We’re currently a team of 20, based in Santa Barbara, and growing quickly. We are venture-backed, and just raised our $12M Series A from world-leading VCs.

Our founders, two ex-Amazon Alexa Product Managers, were tired of manually sifting through customer reviews, support tickets, and bugs while working on Alexa. They understood the importance of listening to customers and prioritizing their requests effectively, but simply had too much feedback to parse through. So, U...