Applied Machine Learning Engineer

About Nooks.ai

Nooks is an AI Sales Assistant Platform (ASAP) that automates administrative tasks, enabling sales representatives to focus on the human aspects of selling and pipeline generation. The platform has supported thousands of sales reps in achieving quota targets, saved customers hundreds of thousands of hours, and powered hundreds of millions of dollars in pipeline value. Nooks is trusted by leading companies including HubSpot, Rippling, and hundreds of other organizations globally.

Backed by over $70M in funding from top-tier venture capital firms including Kleiner Perkins—which made its first sales-tech investment in over a decade by backing Nooks—the company has demonstrated exceptional growth momentum. Over the past two years, the company has achieved 4x then 3x ARR growth, with plans to 3x again this year.

The Role

This is an Applied Machine Learning Engineer position focused on implementing cutting-edge ML features into the Nooks platform. The ideal candidate will have prior industry experience in organizations where machine learning is a core component of the product offering. Your work will directly impact the accuracy and performance of AI-driven sales automation features.

Key Responsibilities:

• Train and optimize production machine learning models to improve accuracy for specific sales use cases
• Align technical strategy with performance, cost, and feasibility considerations
• Develop solutions for realtime audio AI with precision/recall/latency tradeoffs
• Work with audio data, transcription, silence detection, and multi-modal signals to detect conversation context (voicemail vs. human vs. dial tree)
• Leverage LLM embeddings, few-shot learning, and continuous production monitoring
• Implement smart call funnels and playbooks using data wrangling and large language models
• Build conversation embeddings and statistical models to understand sales call patterns

Technical Challenges You May Encounter

Realtime Audio AI & Precision/Recall/Latency Tradeoffs: Develop systems using audio data, transcription, and silence detection to classify live phone calls. You'll balance detection accuracy with latency requirements, employing LLM embeddings, few-shot learning, data labeling, and production performance monitoring.

Smart Call Funnels & Playbooks: Transform unstructured call data into actionable sales strategies. Identify conversation bottlenecks, analyze challenging questions, and design playbooks that help standardize best practices using GPT-3 and other large language models.

Conversation Embeddings & Pattern Analysis: Build statistical models to extract meaningful patterns from sales conversations, enabling better coaching, feedback loops, and performance optimization.

This role offers the opportunity to solve ambitious technical problems in a nascent area—AI-powered realtime collaboration—with significant product impact and real-world applications across thousands of sales teams.

💰 Compensation not publicly listed. Market estimate for similar roles: from $150K, varying by experience and location.