Nooks

What is Nooks?

Nooks is a platform transforming sales reps from manual laborers to scientists. With today’s technology, sales reps shouldn’t need to manually write hundreds of emails, research hundreds of websites/linkedins, and make hundreds of calls. They should instead focus on the parts of their job that actually require people - talking to customers, being creative, and problem-solving. With a combination of AI tools, automation and real-time collaboration, Nooks can do the rest.

About Nooks

  • The team: Nooks is ~45 people. Engineering & product are mostly in SF and go to our office 2-3x/week. The go-to-market team is distributed across the U.S.
    • The founders (Dan, Rohan, and Nikhil) met studying AI at Stanford, have published in top AI journals, Forbes 30u30, worked at Scale AI, Tesla Autopilot, etc.
    • The engineering team has won international math & physics olympiads, has experience at Google, Facebook, Slack, Quora, Scale AI, Bolt, Snap, Flexport, and other fast-growing startups.
    • The sales team have been top-performers at companies like Gong, Amplitude, LeadIQ, and Orum.
  • Fast growth: We’ve grown $0 → ~$4M ARR in 20 months. We grew 4x in 2023 and expect to 3x by EOY 2024.

The problem

Sales pipeline is critical for growing companies. Many, especially B2B companies, have teams of sales/business development representatives (SDR/BDRs) or full-cycle account executives whose responsibility is to identify, contact, and qualify new potential customers. There are ~750,000 SDR/BDR’s in the US alone (e.g. Airtable, Brex, Databricks and many other tech companies have sizable SDR/BDR teams)

In their day-to-day, SDR/BDRs spend time on 3 main activities:

  1. Prospecting & research - identify a list of potential customers using signals like industry, size, fundraising, headcount growth, new hires, job descriptions, etc.
  2. Email & LinkedIn messaging - write messages to those contacts to convey the problem and pitch your product. The goal is for them to book a demo
  3. Calling - Live phone conversations often have higher conversion than emails because they’re more personal, but there’s a lot more manual work involved

Most of the sales rep’s job can be automated with today’s technology: large language models, web scraping, automation, integrations, etc.

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 conversation is headed?
  • Integrations
    • Our dialer integrates with customers’ sales engagement platforms. Every new platform we integrate with, that opens up a larger market for our product. When building integrations, we need to make sure they’re robust, reliable, and well-abstracted.
  • Frontend performance
    • There’s a lot going on in the frontend - WebRTC, Twilio, React rendering, websockets, etc. And people use Nooks throughout the workday, so we need to make sure our app is performant across a wide range of devices

Requirements

    • 3+ years of prior professional experience across engineering and product in a hyper-growth/entrepreneurial/tech environment, preferably in B2B SaaS
    • Minimum 2 years of experience in a tech startup environment
    • Bachelor's degree in Computer Science, Engineering, or a related quantitative field
    • A proven track record of breaking down complicated problems, setting a product roadmap and leading high-impact projects that drive user or revenue growth
    • Strong UX intuitions with the ability to wireframe improved UX flows and to work closely with a designer
    • Excellent communication & collaboration. You’re able to consider different levels of abstraction so your ideas land with your audience. Both for internal collaboration and external customer conversations.
    • Nice to have: prior experience launching and iterating on AI-driven products

    We offer competitive compensation because we want to hire the best people and reward them for their contributions to our mission. We pay all employees competitively relative to market. In compliance with pay transparency laws and in pursuit of pay equity and fairness, we publish salary ranges for our open roles. The target salary range for this role is $140,000 - $240,000. On top of base salary, we also offer equity, generous perks and comprehensive benefits.

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