Applied Machine Learning Engineer, Nooks

• Our ideal candidate will have prior experience working in industry for a business where ML is a core part of the offering
• 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

Desirable

• 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 our first Machine Learning Engineer
• This is a role focused on implementing ML features into Nooks
• 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
• Background in an analytical field like heuristics, data science &/or statistics
• Prior experiences working in both startup and research environments
• 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?