About the job 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 1Password, Fivetran, Greenhouse, and hundreds more. For more information, visit Nooks.ai. Nooks today Our customers use Nooks for most of their day (avg ~3hrs/business day). Nooks currently owns end-to-end workflows around sales calls: AI dialer - automates the manual parts of the calling process: skipping answering machines, leaving voicemails, taking notes, logging calls, even figuring out what to say on a call Analytics - we record, transcribe, and analyze every call. Since these are all outbound calls with little context, these calls follow similar structure - opener, pitch, questions/objections, ask for meeting, etc. So we can answer questions like: “which reps struggle to book the meeting with prospects who showed interest” or “what are the most common objections across each of our key personas” Salesfloor - sales reps & managers can work together throughout the day, listen to each others’ calls, give real-time advice, coaching, shadowing, onboarding, training.Teams that use Nooks often see a 2-3x increase in reps’ productivity within weeks! And we’re working on adding prospecting / research workflows (to-be-announced soon!) The role Our desired candidate must have a passion for AI-driven innovation and sales. Your responsibilities will include: Partnering with our founders and product engineering team to execute on the delivery of new full stack features across the full sales workflow Creating PRDs based on feature requests, user interviews, and issue reports. You’re constantly empathizing with customers and checking back in to make sure we're delivering intuitive value. Owning the tactical work that happens after we ship new features i.e. user research, teaching users, creation of guides, measuring/ensuring we're tracking the right product metrics etc. Scaling new features from the ground-up by translating commercial business needs into technical solutions. You should have some design thinking/design chops as this will entail creating flows, wireframes, prototypes, and high-fidelity visuals for your features. You’ll partner closely with our amazing designer. A key part of your work will entail understanding user frustrations/pleasures, testing those hypotheses and ultimately translating user needs into AI-driven experiences that augment, enhance and automate manual workflows. 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. Here Is a Non-exhaustive List Of The Types Of Problems We’re Working On Concurrency & distributed systems Our smart dialer places calls in parallel and runs a realtime AI model on each call. There are some interesting concurrency problems syncing state between Twilio, our backend, and the frontend, and knowing which calls to connect, which to continue in the background, and when to start the next call. 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. Latency (infrastructure) If our model took 5 seconds to detect a human on a phone call, the human would hang up. It’s imperative we can detect quickly and that our users can execute calls quickly. There’s latency across the detection pipeline including transcription models, audio models, websockets, Twilio API, database transactions, etc. 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