Sr Engineer, ML, SMAI

Micron•Boise, ID

10d

About The Position

Build and maintain data and solution pipeline engineering to provide robust, scalable infrastructure for training and deploying machine learning models. Analyze Micron datasets for patterns, trends, and insights; develop and iterate on machine learning models for business challenges and product functionality; and collaborate on data preprocessing and feature engineering. Design and optimize data structures in cloud data management systems, including Snowflake, GCP, and Azure, and build custom software components and analytics applications. Deploy machine learning models into production, select models that balance performance with compute costs, monitor deployed-model performance, and maintain CI/CD pipelines for cloud-based machine learning solutions. Work with Product and Engineering teams to identify opportunities for generative AI in projects and platforms, and with Product teams to develop a tactical roadmap for adopting generative AI technologies, paradigms, and frameworks in line with organizational best practices. Design, develop, and deploy enterprise-grade applications using generative AI technologies, including LLMs, multimodal architectures, and advanced prompt engineering. Work includes fine-tuning pre-trained models on domain-specific datasets, implementing retrieval-augmented generation (RAG) for knowledge grounding, and building agentic systems that orchestrate autonomous workflows and integrate with domain-specific APIs.

Requirements

Master’s degree in Computer Science, Machine Learning, Data Science, Statistics, Industrial Engineering, or a related field, plus 1 year of experience in the job offered or a related occupation; alternatively, a bachelor’s degree in one of these fields plus 3 years of experience.
Experience with enterprise-grade, end-to-end machine learning systems in cloud environments.
Experience with regression and classification algorithms, deep learning, reinforcement learning, and generative AI.
Experience with event-driven pipelines using Pub/Sub, Solace, or Kafka.
Experience with Docker and Kubernetes.
Experience with ETL/ELT pipelines using Kubeflow, Dataflow, or Airflow.
SQL skills.
Experience developing RESTful APIs for machine learning models.
Experience with LLMs, multimodal models, prompt engineering, and agentic systems.
Experience with retrieval-augmented generation (RAG) and fine-tuning domain-specific models.
Experience with semiconductor manufacturing big data.

Responsibilities

Build and maintain data and solution pipeline engineering for scalable machine learning training and deployment infrastructure.
Analyze datasets to identify patterns, trends, and insights that can improve machine learning model development.
Design, implement, and iterate on machine learning models for business challenges and product functionality.
Collaborate on data preprocessing and feature engineering to improve model input quality.
Design and optimize data structures in cloud data management systems, including Snowflake, GCP, and Azure.
Build custom software components and analytics applications.
Deploy machine learning models into production environments and select models that balance performance with compute costs.
Establish and maintain monitoring systems for deployed models and continuous improvement.
Create and maintain cloud-based CI/CD pipelines for machine learning solutions.
Collaborate with Product and Engineering teams to identify generative AI integration opportunities in project solutions and platforms.
Collaborate with Product teams on a tactical roadmap for adopting generative AI technologies, paradigms, and frameworks.
Design, develop, and deploy enterprise-grade applications using generative AI technologies, including LLMs, multimodal architectures, and prompt engineering.
Fine-tune pre-trained models on domain-specific datasets, implement RAG for knowledge grounding, and build agentic systems that orchestrate workflows and integrate with domain-specific APIs.