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:
- Prospecting & research – identify a list of potential customers using signals like industry, size, fundraising, headcount growth, new hires, job descriptions, etc.
- 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
- 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!)
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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. We’re hiring our first Machine Learning Engineer. 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?
Requirements
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field.
- 3+ years of industry experience, including 2+ years training and deploying ML models in production.
- Full stack ML Eng chops: proficiency in general purposes programming languages such as Python/Javascript, and with libraries like TensorFlow, PyTorch, Keras, scikit-learn etc.
- Expertise in areas like NLP, Deep Learning, Anomaly Detection, Transformers and Large Language Models.
Nice to haves:
- Background in an analytical field like heuristics, data science &/or statistics
- Prior experiences working in both startup and research environments
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.
Natural language processing (NLP) mathematics Facebook latency Artificial intelligence (AI) gpt-3 automation precision quora data-wrangling anomaly-detection User Experience (UX) physics web-scraping deep-learning Collaboration Google Machine Learning Sales algorithms Slack LLM