Principal Data Scientist-Gen AI, Machine Learning
Extreme NetworksCloud Networking company
Seattle, United StatesLead
Norwest Venture Partners
Kleiner Perkins Caufield & Byers
AVI Capital Management
Trinity Ventures
Data & AI
About the role
TL;DR
Lead data science and ML strategy for Generative AI and network management.
- •Principal Data Scientist
- •(Gen AI, Machine Learning): Are you energized by the idea of innovating with Generative AI? Do you want to create global impact while tackling challenges at the forefront of Artificial Intelligence? Do you dream of building ground-breaking products that define the future of AI-driven network management? Then come, advance with us at Extreme.
- •This is a greenfield opportunity to shape next-gen networking experiences at the cutting edge of Generative AI, Machine Learning, Big Data, and Cloud Computing.
- •You will help define every aspect of the user journey, product vision, and technical roadmap, and you will drive innovation from concept to delivery.
- •There has never been a better time to join Extreme.
- •With multiple acquisitions expanding our portfolio and market strategy, we are experiencing unprecedented growth worldwide.
- •Recognized as a Technology Leader in the Gartner Magic Quadrant and a multi-year Best Employer award winner, Extreme is committed to a culture of diversity, inclusion, and equality, where every employee thrives because of their differences, not despite them.
- •Our AI Core group is pioneering platforms and solutions for Generative AI, including AI Agents, RAG, Knowledge Bases, Data Mining, Anomaly Detection, and LLM fine-tuning.
- •These innovations power flagship Extreme products while enabling entirely new offerings.
- •Together, we are driving a fundamental shift in how businesses manage networks by building intelligent, high-performance multi-agent systems that perceive, learn, and act in real time.
- •At Extreme, innovation is not just encouraged, it is expected.
- •Advance with us and help shape the future of network intelligence.
- •Job Responsibilities Define and drive the long-term data science and ML strategy, influencing both product direction and organizational priorities Lead high-impact research initiatives in ML, GenAI, and Graph ML, push the boundaries of applied science, and establish best practices for scalable adoption Partner with engineering and product leadership to align data science innovation with business goals, shaping platform and infrastructure investments Mentor and guide staff
- •and senior-level scientists, set technical direction, and foster a culture of excellence and innovation Represent the organization externally through publications, talks, and collaborations, strengthening the company’s thought leadership in AI and ML Requirements: Degree in Computer Science, Mathematics, or a related field 8+ years of experience in applied ML research and production deployment 3+ years of hands-on experience building Generative AI solutions such as RAG, AI Agents, or LLM fine-tuning in production Experience with Graph ML and Graph technologies such as GNNs or GraphRAG in production Proven track record of end-to-end ownership including design, experimentation, validation, deployment, and scaling of ML systems Experience deploying solutions on cloud platforms such as AWS, Azure, or GCP Demonstrated ability to solve highly complex, ambiguous, cross-domain problems with measurable business impact Preferred Qualifications MS or PhD in Computer Science, Machine Learning, or a related discipline Experience with distributed Big Data and ML platforms such as Spark, Flink, Kafka, PySpark, or Lakehouse Recognized track record in the ML and AI community through publications, patents, open-source contributions, or conference talks Strong ability to influence at the organizational level by driving strategy and fostering cross-functional alignment
Required skills
Generative AIMachine LearningBig DataCloud ComputingAI AgentsRAGKnowledge BasesData MiningAnomaly DetectionLLM fine-tuningGraph MLGNNsGraphRAGAWSAzureGCPSparkFlinkKafkaPySpark
Tech stack
Generative AIMachine LearningBig DataCloud ComputingAI AgentsRAGKnowledge BasesData MiningAnomaly DetectionLLM fine-tuningGraph MLGNNsGraphRAGAWSAzureGCPSparkFlinkKafkaPySpark