Product Engineer, Nooks
New Graduate
$140-155k
+ Offers Equity
AI-powered parallel dialer & virtual salesfloor
Job no longer available
AI-powered parallel dialer & virtual salesfloor
201-500 employees
Job no longer available
$140-155k
+ Offers Equity
201-500 employees
Company mission
To automate this grunt work so salespeople can focus on the human parts of the job.
Role
Who you are
- Some practical software engineering experience garnered from industry internships
- 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
- Experience building complex systems (ideally somewhat related to ours)
- You’re a confident, independent, and experienced engineer who is used to extreme ownership and solving hard problems
What the job involves
- 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 talented full-stack/backend/ML engineers who are product-minded and excited to delight our customers
- We expect every software engineer on our team to be able to work within a complex code-base, own entire product areas, and build new features end-to-end
- 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
Our take
For many businesses, the rise in hybrid and remote working has lessened team building and collaboration within their teams. Nooks was founded in 2020, initially as a virtual space for sales teams to collaborate and work in real-time, and to feel like they're working in the same room as their colleagues.
Nooks evolved from that social workflow premise and developed an AI dialer to automate manual tasks for sales teams. It also has a "Call Analytics" tool for sales teams to transcribe, analyse and improve upon their calls. Meanwhile, its virtual salesfloor still makes collaborating, team building and training easier for virtual teams.
While Nooks is up against large workflow management platforms such as Slack, its tight focus on sales teams lets it stand out. This speciality has led to several rounds of funding for Nooks, two of which came in 2024 off the back of massive momentum and growth for the company.
Freddie
Company Specialist at Welcome to the Jungle
Insights
Company
Funding (last 2 of 3 rounds)
Oct 2024
$43m
SERIES B
Apr 2024
$22m
SERIES A
Total funding: $70m
Company HQ
Polk Gulch, San Francisco, CA
Leadership
Rohan Suri
(Co-Founder & Sales Leader)Previously a Software Engineering Intern at Forethought, and ML Engineer Intern at Cerebras Systems. Was also a Machine Learning Researcher at Stanford Artificial Intelligence Laboratory and a Cybersecurity Intern at Visa.
Nikhil Cheerla
(Co-Founder & CTO)Previously an ML Engineer at Tesla. Was also a Vision Lab Research Intern at Stanford University, and Engineering Intern at Lyft and Quora.
Dan L
(Co-Founder & CEO)Previously a Machine Learning Engineer at Scale AI. Worked in Quantitative Strategies at IEX Group and ML Research at Cerebras Systems.
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