About the job
About Nooks.ai:
Nooks is a cutting-edge AI Sales Assistant Platform (ASAP) designed to streamline sales processes, allowing representatives to concentrate on building relationships and closing deals. Our innovative platform has empowered thousands of sales professionals to achieve their targets, saving clients countless hours and generating substantial revenue. Trusted by sales teams at industry leaders like Hubspot, Rippling, and Toast, Nooks is transforming the sales landscape.
Backed by over $70M in investments from top-tier venture capital firms, including Kleiner Perkins, Nooks has experienced remarkable growth, achieving a 4x and 3x increase in ARR over the past two years. We are on an ambitious trajectory to triple our growth once again this year.
For more information, visit Nooks.ai.
The Role
Note: Job title will be aligned with candidate experience.
We are seeking a passionate Applied Machine Learning Engineer to join our dynamic team, tackling exciting technical challenges in the emerging field of AI-powered real-time collaboration. This role is pivotal in integrating machine learning features into the Nooks platform. The ideal candidate will have hands-on experience in a business where machine learning plays a central role.
Key responsibilities will involve training production models to enhance their accuracy for specific sales applications, while aligning our technical strategy with performance, cost, and feasibility factors.
Examples of Engineering Challenges You Might Encounter
These examples are illustrative; prior experience in all areas is not required. We hope you find some of these challenges intriguing!
Real-time Audio AI & Precision/Recall/Latency Trade-offs (Algorithms & Models)
Utilizing audio data, transcription, silence detection, and multiple signals to discern if a live call is a voicemail, a human, or a dial tree. Managing latency alongside precision/recall trade-offs is crucial for prompt human detection, involving advanced techniques like LLM embeddings, few-shot learning, data labeling, and continuous performance monitoring.
Intelligent Call Funnels & Playbooks (Data Wrangling, Backend Engineering, GPT-3, UX)
Analyzing the conversational flow to optimize call funnels and playbook strategies, focusing on data visibility and user experience.

