Getnooks
Applied Machine Learning Engineer
Getnooks, California, Missouri, United States, 65018
The role
Note: Exact job title will be commensurate with experienceWe 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. Our ideal candidate will have prior experience working in industry for a business where ML is a core part of the offering.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.Examples of engineering problems you may touch
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!
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?
Requirements
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.Nice to haves:Background in an analytical field like heuristics, data science &/or statisticsPrior experiences working in both startup and research environmentsWe offer competitive compensation because we want to hire the best people and reward them for their contributions to our mission. We pay all employees competitively relative to market. In compliance with pay transparency laws and in pursuit of pay equity and fairness, we publish salary ranges for our open roles. The target salary range for this role is $140,000 - $240,000. On top of base salary, we also offer equity, generous perks and comprehensive benefits.
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Note: Exact job title will be commensurate with experienceWe 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. Our ideal candidate will have prior experience working in industry for a business where ML is a core part of the offering.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.Examples of engineering problems you may touch
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!
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?
Requirements
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.Nice to haves:Background in an analytical field like heuristics, data science &/or statisticsPrior experiences working in both startup and research environmentsWe offer competitive compensation because we want to hire the best people and reward them for their contributions to our mission. We pay all employees competitively relative to market. In compliance with pay transparency laws and in pursuit of pay equity and fairness, we publish salary ranges for our open roles. The target salary range for this role is $140,000 - $240,000. On top of base salary, we also offer equity, generous perks and comprehensive benefits.
#J-18808-Ljbffr