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# Senior Machine Learning Engineer

- **Company:** Extreme Networks
- **Location:** Seattle, WA, United States
- **Experience:** Expert
- **Salary:** $185,000.0 - $230,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Big Data, Distributed Systems, Machine Learning, Systems Development Life Cycle, Azure Machine Learning, Software Engineering, Large Language Models, Apache Spark, Generative AI, Pyspark, Information Technology, Druid, Apache Flink, Apache Kafka, Machine Learning Operations
- **Published:** July 26, 2026
- **Apply:** https://www.careerjet.com/jobad/usf1f0e53db46672b6c97aabbccab7a10d

## About the Role

· Degree in Computer Science, Mathematics, or a related field
· 5+ years of experience across the full SDLC: design, coding, reviews, testing, deployment, and operations
· 5+ years of experience architecting and deploying end-to-end ML solutions in production environments
· Proven expertise developing Generative AI solutions such as RAG, AI Agents, and LLM fine-tuning at scale
· Strong background in building and operating large-scale distributed systems on cloud platforms such as AWS, Azure, or GCP
· Demonstrated ability to solve highly complex and ambiguous problems, setting direction for others
Preferred Qualifications
· MS or PhD in Computer Science, Machine Learning, or a related discipline
· Experience with Graph ML and graph technologies such as GNNs or Graph RAG
· Deep expertise with distributed Big Data technologies such as Spark, Flink, Kafka, PySpark, Lakehouse, Druid, Hudi, or Glue
· Track record of mentoring engineers and influencing cross-team initiatives

## Description

· Serve as a thought leader and forward thinker, setting the technical vision and driving innovation across products and platforms. Shape long-term strategy by designing and launching strategic ML solutions that deliver company-wide impact.
· Own and guide the full software development lifecycle at scale, including architecture, design, testing, deployment, and operations. Lead technical discussions, define best practices, and ensure engineering rigor through design and code reviews.
· Architect and deliver high-performance, production-grade ML platforms and frameworks, enabling next-generation real-time ML and Generative AI systems.
· Partner with senior engineers, scientists, and cross-functional leaders to accelerate experimentation, validation, and model integration, ensuring solutions are robust, scalable, and aligned with business goals.


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