About the role
About Nooks.ai : Nooks is the AI Sales Assistant Platform (ASAP) that automates the busywork so reps can focus on the human part of selling and generate more sales pipeline. Nooks has helped thousands of sales reps hit quota, saved customers hundreds of thousands of hours, and powered hundreds of millions of dollars in pipeline. Nooks is loved by sales teams at companies like Hubspot, Rippling, and hundreds more. We’re a team of high performers raising over $70M from top VCs, including Kleiner Perkins, which made its first sales-tech investment in over 10 years by investing in Nooks. Over the past two years, we’ve grown ARR by 4x and then 3x, and we plan to 3x it again this year. For more information, visit Nooks.ai .
The role Note: Exact job title will be commensurate with experience We have an ambitious product vision in a nascent area - AI-powered realtime collaboration - so there are a ton of interesting technical challenges on our roadmap. This is a role focused on implementing ML features into Nooks. Our ideal candidate will have prior experience working in industry for a business where ML is a core part of the offering.
Responsibilities will include training production models to improve their accuracy for specific sales use cases. You will align our technical strategy with performance, cost and feasibility considerations. Examples of engineering problems you may touch These are just examples, this list is non-exhaustive, and you definitely don’t need experience in all of these areas. But hopefully you find some of them exciting! Realtime audio AI & precision/recall/latency tradeoffs (algorithms & models) We use audio data, transcription, silence detection, and several other signals to detect whether a live phone call is a voicemail, a human, or a dial tree. Here, latency is a third factor added to the standard precision/recall tradeoff because it’s important we can detect humans quickly. Our approach involves LLM embeddings, few-shot learning, data labeling, and continuous monitoring of model performance in prod. Smart call funnels & playbooks (data wrangling, backend eng, GPT-3, UX) At what point in the conversation do my reps get stuck? What are the toughest questions that we need to address? Can I “program” a playbook so that Nooks will help my team standardize toward best-practices? We’re using GPT-3 and other LLM’s to turn companies’ mostly unstructured call data into actionable strategies & feedback loops. Conversation embeddings & markov models (ML modeling) What does the anatomy of a call look like? If I say XYZ, what are the different ways the prospect might answer and the probabilities of each? Conditioned on the first half of the call, what do I say next to maximize the likelihood that I book a demo at the end of the call? Can we use LLM’s to generate embeddings of conversations that we can use to cluster similar conversation patterns and predict where the conversation is headed?