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 ~30 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.
  • Fundraising: we’ve raised ~$20M in venture capital from top-tier investors. $15M in a recent (un-announced) Series A
  • Fast growth: in 16 months (since June ‘22) we’ve grown $0 → $2.5M ARR. So far in 2023 we’ve grown over 3x and expect to 4x by EOY.

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

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. We expect members of our engineering team, including our Engineering Manager, to be able to work within a complex code-base, own entire product areas, and build new features end-to-end.

Examples of engineering problems we’re working on

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!

  • 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

We are looking for a hands-on technical leader that has either already 1). served in a people management capacity or 2). acted as a tech lead and holds an interest in people management. You will have likely done this in a fast-growing, fast-moving early stage start up environment. You have ideally seen and played a role in creating a results-driven, execution-focused engineering culture where all team members are required to drive product goals with wide surface area.

Responsibilities:

  1. Lead, mentor, and inspire a team of engineers, fostering a high-performing culture.
  2. Technical Expertise: You will leverage your strong full stack technical knowledge and expertise to guide the team by example, actively contributing to our most important engineering projects.
  3. Project Management: Drive the successful delivery of projects, ensuring adherence to timelines, quality standards, and stakeholder expectations. Collaborate with cross-functional teams to define project scope, milestones, and resource allocation.
  4. Talent Acquisition: You will partner with our Talent Lead and Co-Founders to build a world-class engineering team. This will entail creating a scalable hiring process, attract top engineering talent, conduct interviews and ensuring we make progress against our goals.
  5. Performance Management: You will be responsible for setting clear performance expectations for team members, providing ongoing feedback and performance evaluations.
  6. Process Improvement: Continuously evaluate and enhance engineering processes and automate manual workflow for maximum productivity.

Requirements:

  • Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
  • 5+ years of experience as a software engineer or in a related technical role (job title will be commensurate with experience).
  • Ability to work with our tech stack:
      • Frontend: React, Typescript, MobX
      • Backend: Node.js, Express, Typescript
      • Technologies: Firebase, Firestore, Websockets, Twilio, WebRTC, Postgres, Redis
      • ML: GPT, Transformers, PyTorch, signal processing, few-shot classification.
  • 2+ years of experience in a people management of tech lead role, preferably in a start-up or fast-paced environment.
  • Proven track record of successfully managing engineering projects/leading teams whilst also making individual contributions.
  • Excellent communication and interpersonal skills, with the ability to effectively collaborate with cross-functional teams.

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 $160,000 - $240,000. On top of base salary, we also offer equity, generous perks and comprehensive benefits.

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