Rippling
Principal Data Scientist
Rippling, Atherton, California, United States, 94027
Rhythms is an early stage startup developing an AI-powered platform to redefine organizational efficiency and teamwork. Utilizing cutting-edge AI, machine learning, and natural language processing technologies, we aim to elevate team dynamics to achieve unparalleled productivity and business outcomes. If you are driven by the challenge of harnessing AI to transform the future of how teams work, we invite you to apply.We are in search of a Principal Data Scientist with a Ph.D. and proven experience in developing Large Language Models (LLMs) with hands-on experience in frameworks such as LangChain, Llamaindex, and a strong background in designing and implementing multi-agent systems. The successful candidate will drive the development of AI algorithms and models, focusing on enhancing our platform's core capabilities through intelligent orchestration and optimization of work rhythms.Skills
Ph.D. in Computer Science, Artificial Intelligence, or a related discipline, with a specialization in Large Language Models.Over 10 plus years of experience in data science or AI, including pivotal contributions to AI initiatives within top-tier tech companies.Expertise in Python or R, and familiarity with AI/ML libraries and frameworks such as TensorFlow, PyTorch, LangChain, LLaMA, with particular emphasis on multi-agent systems.Strong foundation in NLP, predictive modeling, statistical analysis, and machine learning algorithms, with a portfolio showcasing innovative AI solutions.Key Responsibilities
Design and develop advanced AI models, focusing on LLMs and applying frameworks like LangChain, LlamaIndex, to conduct in-depth analyses of team dynamics and organizational patterns.Innovate in the area of multi-agent architectures to facilitate sophisticated interaction models within the platform, enhancing collaboration and efficiency.Work closely with engineers and designers ensuring AI integrations deliver optimal user experience and contribute to the platform's strategic goals.Adopt and apply the latest advancements in AI, ML, and NLP to continuously push the boundaries of the platform's intelligence and functionality.Lead data-driven analysis and modeling efforts to derive insights that significantly boost team productivity and effectiveness.Establish a robust, agile data science lifecycle from data pre-processing to model deployment, emphasizing swift iterations and adaptability.Foster a culture of technical excellence, innovation, and lifelong learning within the team, staying at the forefront of AI research and applications.
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Ph.D. in Computer Science, Artificial Intelligence, or a related discipline, with a specialization in Large Language Models.Over 10 plus years of experience in data science or AI, including pivotal contributions to AI initiatives within top-tier tech companies.Expertise in Python or R, and familiarity with AI/ML libraries and frameworks such as TensorFlow, PyTorch, LangChain, LLaMA, with particular emphasis on multi-agent systems.Strong foundation in NLP, predictive modeling, statistical analysis, and machine learning algorithms, with a portfolio showcasing innovative AI solutions.Key Responsibilities
Design and develop advanced AI models, focusing on LLMs and applying frameworks like LangChain, LlamaIndex, to conduct in-depth analyses of team dynamics and organizational patterns.Innovate in the area of multi-agent architectures to facilitate sophisticated interaction models within the platform, enhancing collaboration and efficiency.Work closely with engineers and designers ensuring AI integrations deliver optimal user experience and contribute to the platform's strategic goals.Adopt and apply the latest advancements in AI, ML, and NLP to continuously push the boundaries of the platform's intelligence and functionality.Lead data-driven analysis and modeling efforts to derive insights that significantly boost team productivity and effectiveness.Establish a robust, agile data science lifecycle from data pre-processing to model deployment, emphasizing swift iterations and adaptability.Foster a culture of technical excellence, innovation, and lifelong learning within the team, staying at the forefront of AI research and applications.
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