Applied Machine Learning Engineer at 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.

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.

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?

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.