Product Engineer, Nooks
New Graduate
$140-155k
+ Equity
AI-powered parallel dialer & virtual salesfloor
Be an early applicant
AI-powered parallel dialer & virtual salesfloor
21-100 employees
Be an early applicant
$140-155k
+ Equity
21-100 employees
Company mission
To automate this grunt work so salespeople can focus on the human parts of the job.
Role
Who you are
- 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
- Ability to work with our stack:
- Some practical software engineering experience garnered from industry internships
- 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
- 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 wsho are product-minded and excited to delight our customer
- 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!
- 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
- 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
- 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
- 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
- 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?
- 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
- 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, the latest of which is being channelled into expanding operations and business reach.
Freddie
Company Specialist at Welcome to the Jungle
Insights
Few candidates hear
back within 2 weeks
Company
Funding (last 2 of 3 rounds)
Oct 2024
$43m
SERIES B
Apr 2024
$22m
SERIES A
Total funding: $70m
Company values
- Earn customer love - Nooks should be the tool our customers love the most. We earn customer love with every interaction, every detail, and every customer problem we solve
- Extreme ownership - We can all drive outcomes and enact change. We take responsibility, step out of our comfort zone, overcome obstacles, and do the hard work to deliver results
- Do more with less - Speed, efficiency, and ruthless prioritization are our competitive advantages. We ship & iterate quickly while wearing multiple hats. We can do anything, but not everything
- Ask why - Deeply understand customer problems. Question assumptions. We expect that others will ask “why” so we back up decisions with clear reasoning
- Energize & support the team - Energy is contagious. We aim to electrify everyone around us with our positivity & passion. We lead by example - no job is beneath us. We succeed as a team
- Be a good person - We want to work with high integrity people. Good things happen to good people. Be kind, humble, honest, ethical, and trustworthy. Act with empathy and choose ‘right’ over ‘easy’
Company HQ
Polk Gulch, San Francisco, CA
Leadership
Rohan Suri
(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
(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
(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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