Staff Machine Learning Engineer

Micron Technology • Boise, ID

2d • Hybrid

About The Position

Our vision is to transform how the world uses information to enrich life for all. Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. Analyze existing data sets to identify patterns, trends, and insights that can enhance machine learning model development. Design, implement, and iterate on machine learning models to address specific business challenges and enhance product functionality. Build knowledge by keeping up with the latest advancements in machine learning and artificial intelligence (AI), integrating new techniques and technologies into our MLOPS development process. Build and maintain Data/Solution Pipeline to ensure a robust and scalable data infrastructure that supports the training and deployment of machine learning models. Collaborate on data preprocessing and feature engineering to enhance the quality of input data for machine learning models. Build custom software components and analytics applications. Create/Maintain CI/CD pipelines of machine learning solutions in the cloud environment. Implement strategies for deploying machine learning models into production environments. Responsible for selecting the best model to meet both model performance and minimize compute costs. Establish and maintain monitoring systems to track the performance of deployed models and facilitate continuous improvement. Collaborate with the Product and Engineering teams to identify opportunities for integrating machine learning and Generative artificial intelligence capabilities into Project solutions/platform. Collaborate with the Product team to design a Tactical roadmap for the adoption of machine learning technologies, paradigms & frameworks following organizational best practices. Work in a technical team through development, deployment, and application of applied analytics, predictive analytics, and prescriptive analytics using machine learning and artificial intelligence. Build enterprise-grade applications leveraging Generative AI technologies, including large language models (LLMs), multimodal architectures, and advanced prompt engineering. Fine-tune pre-trained models on domain-specific datasets, Implement retrieval-augmented generation (RAG) for knowledge grounding, and build agentic systems that orchestrate autonomous workflows and integrate with domain-specific APIs. May telecommute part-time.

Requirements

• Building and executing end to end ML systems
• TensorFlow, PyTorch, and scikit learn.
• Prediction (regression) and classification algorithms; deep learning, reinforcement learning, and generative AI
• Python or Java
• Pub/Sub, Solace, or Kafka
• Developing ML/AI solutions in GCP, Azure, or AWS
• Docker and Kubernetes
• Developing ETL/ELT pipelines using Kubeflow, Dataflow, or Airflow
• SQL
• Data structures and schemas
• APIs to expose trained machine learning models for consumption
• Building enterprise-grade applications using Generative AI technologies
• Retrieval-Augmented Generation (RAG) for knowledge grounding and fine-tuning pre-trained models on domain-specific datasets
• Designing and implementing agentic systems that orchestrate autonomous workflows and integrate with APIs

Responsibilities

• Analyze existing data sets to identify patterns, trends, and insights that can enhance machine learning model development.
• Design, implement, and iterate on machine learning models to address specific business challenges and enhance product functionality.
• Build knowledge by keeping up with the latest advancements in machine learning and artificial intelligence (AI), integrating new techniques and technologies into our MLOPS development process.
• Build and maintain Data/Solution Pipeline to ensure a robust and scalable data infrastructure that supports the training and deployment of machine learning models.
• Collaborate on data preprocessing and feature engineering to enhance the quality of input data for machine learning models.
• Build custom software components and analytics applications.
• Create/Maintain CI/CD pipelines of machine learning solutions in the cloud environment.
• Implement strategies for deploying machine learning models into production environments.
• Responsible for selecting the best model to meet both model performance and minimize compute costs.
• Establish and maintain monitoring systems to track the performance of deployed models and facilitate continuous improvement.
• Collaborate with the Product and Engineering teams to identify opportunities for integrating machine learning and Generative artificial intelligence capabilities into Project solutions/platform.
• Collaborate with the Product team to design a Tactical roadmap for the adoption of machine learning technologies, paradigms & frameworks following organizational best practices.
• Work in a technical team through development, deployment, and application of applied analytics, predictive analytics, and prescriptive analytics using machine learning and artificial intelligence.
• Build enterprise-grade applications leveraging Generative AI technologies, including large language models (LLMs), multimodal architectures, and advanced prompt engineering.
• Fine-tune pre-trained models on domain-specific datasets.
• Implement retrieval-augmented generation (RAG) for knowledge grounding.
• Build agentic systems that orchestrate autonomous workflows and integrate with domain-specific APIs.

Benefits

• Choice of medical, dental and vision plans
• Benefit programs that help protect your income if you are unable to work due to illness or injury
• Paid family leave
• Robust paid time-off program
• Paid holidays

What This Job Offers

• Job Type: Full-time
• Career Level: Senior