Getnooks is hiring: Applied Machine Learning Engineer Engineering in San Francis
Getnooks, San Francisco, CA, United States
About Nooks.ai : Nooks is the AI Sales Assistant Platform (ASAP) that automates the busywork so reps can focus on the human part of selling and generate more sales pipeline. Nooks has helped thousands of sales reps hit quota, saved customers hundreds of thousands of hours, and powered hundreds of millions of dollars in pipeline. Nooks is loved by sales teams at companies like 1Password, Fivetran, Greenhouse, and hundreds more. For more information, visit Nooks.ai .
The role Note: Exact job title will be commensurate with experience
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. 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 statistics
Prior experiences working in both startup and research environments
We 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.
Equal Employment Opportunity Statement Nooks is an equal opportunity employer committed to fostering a diverse and inclusive workforce. We believe in providing equal employment opportunities to all individuals regardless of race, color, religion, gender, gender identity, sexual orientation, national origin, age, disability, veteran status, or any other characteristic protected by law.
Nooks does not discriminate in hiring, promotion, compensation, or any other employment practices, and we are committed to ensuring a workplace that is free from discrimination, harassment, and retaliation. We encourage individuals from all backgrounds to apply and join our team.
#J-18808-Ljbffr
In Summary: Nooks is the AI Sales Assistant Platform (ASAP) that automates the busywork so reps can focus on the human part of selling . The ideal candidate will have prior experience working in industry for a business where ML is a core part of the offering . The target salary range for this role is $140,000 - $240,000 .
En Español: Nooks ha ayudado a miles de representantes de ventas a alcanzar la cuota, ahorró cientos de miles de horas a los clientes y alimentó ciertos millones de dólares en la tubería. Nooks es amada por los equipos de venta en compañías como 1Password, Fivetran, Greenhouse y cientos más. Para obtener más información, visite Nooks. La función Nota: Título exacto del trabajo será proporcional con la experiencia Tenemos una visión ambiciosa de producto en un área naciente - tonificación en tiempo real impulsada por IA - así que hay ejemplos de interesantes desafíos técnicos en nuestra hoja de ruta. Este es un papel enfocado en implementar características técnicas de ML en Nooks . Pero esperamos que encuentres algunas de ellas emocionantes! AI audio en tiempo real y precisión/recall/latency tradeoffs (algoritmos & modelos) Utilizamos datos de audio, transcripción, detección del silencio y varias otras señales para detectar si una llamada telefónica en vivo es un correo vocal, un humano o un árbol de dial. Aquí, la latencia es un tercer factor añadido al compromiso estándar de precisión /recall porque es importante que podamos detectar a los humanos rápidamente. Nuestro enfoque implica incorporaciones de LLM, aprendizaje a pocas capturas, etiquetado de datos y monitoreo continuo del rendimiento del modelo en prod. Funillas de llamadas inteligentes & playbooks (data wrangling, backend eng, GPT-3, UX) En qué momento de la conversación se quedan atascados mis representantes? ¿Cuáles son las preguntas más difíciles que debemos abordar? ¿Puedo programar un libro de juego para que Nooks ayude a mi equipo a estandarizar hacia las mejores prácticas? Nooks es un empleador de igualdad de oportunidades comprometido a fomentar una fuerza laboral diversa e inclusiva. Creemos en proporcionar oportunidades laborales iguales a todas las personas independientemente de su raza, color, religión, género, identidad sexual, orientación sexual, origen nacional, edad, discapacidad, estatus veterano o cualquier otra característica protegida por la ley. Nooks no discrimina en contratación, promoción, compensación u otras prácticas de empleo, y estamos comprometidos a garantizar que el lugar de trabajo esté libre de discriminación y discriminación.
The role Note: Exact job title will be commensurate with experience
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. 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 statistics
Prior experiences working in both startup and research environments
We 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.
Equal Employment Opportunity Statement Nooks is an equal opportunity employer committed to fostering a diverse and inclusive workforce. We believe in providing equal employment opportunities to all individuals regardless of race, color, religion, gender, gender identity, sexual orientation, national origin, age, disability, veteran status, or any other characteristic protected by law.
Nooks does not discriminate in hiring, promotion, compensation, or any other employment practices, and we are committed to ensuring a workplace that is free from discrimination, harassment, and retaliation. We encourage individuals from all backgrounds to apply and join our team.
#J-18808-Ljbffr
In Summary: Nooks is the AI Sales Assistant Platform (ASAP) that automates the busywork so reps can focus on the human part of selling . The ideal candidate will have prior experience working in industry for a business where ML is a core part of the offering . The target salary range for this role is $140,000 - $240,000 .
En Español: Nooks ha ayudado a miles de representantes de ventas a alcanzar la cuota, ahorró cientos de miles de horas a los clientes y alimentó ciertos millones de dólares en la tubería. Nooks es amada por los equipos de venta en compañías como 1Password, Fivetran, Greenhouse y cientos más. Para obtener más información, visite Nooks. La función Nota: Título exacto del trabajo será proporcional con la experiencia Tenemos una visión ambiciosa de producto en un área naciente - tonificación en tiempo real impulsada por IA - así que hay ejemplos de interesantes desafíos técnicos en nuestra hoja de ruta. Este es un papel enfocado en implementar características técnicas de ML en Nooks . Pero esperamos que encuentres algunas de ellas emocionantes! AI audio en tiempo real y precisión/recall/latency tradeoffs (algoritmos & modelos) Utilizamos datos de audio, transcripción, detección del silencio y varias otras señales para detectar si una llamada telefónica en vivo es un correo vocal, un humano o un árbol de dial. Aquí, la latencia es un tercer factor añadido al compromiso estándar de precisión /recall porque es importante que podamos detectar a los humanos rápidamente. Nuestro enfoque implica incorporaciones de LLM, aprendizaje a pocas capturas, etiquetado de datos y monitoreo continuo del rendimiento del modelo en prod. Funillas de llamadas inteligentes & playbooks (data wrangling, backend eng, GPT-3, UX) En qué momento de la conversación se quedan atascados mis representantes? ¿Cuáles son las preguntas más difíciles que debemos abordar? ¿Puedo programar un libro de juego para que Nooks ayude a mi equipo a estandarizar hacia las mejores prácticas? Nooks es un empleador de igualdad de oportunidades comprometido a fomentar una fuerza laboral diversa e inclusiva. Creemos en proporcionar oportunidades laborales iguales a todas las personas independientemente de su raza, color, religión, género, identidad sexual, orientación sexual, origen nacional, edad, discapacidad, estatus veterano o cualquier otra característica protegida por la ley. Nooks no discrimina en contratación, promoción, compensación u otras prácticas de empleo, y estamos comprometidos a garantizar que el lugar de trabajo esté libre de discriminación y discriminación.