Principal Machine Learning Engineer

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.

The Smart Manufacturing and AI team at Micron Technology is looking for an ambitious Machine Learning Engineer. Are you curious, high velocity, and ready to solve complex problems? Do you dream in data science and machine learning models? If so, we want you to join us!

Our mission is to enable to deliver industry-winning machine learning solutions to power Micron’s dominance in the highly competitive memory solutions market! As a Principal Machine Learning Engineer, will have experience in a variety of data and cloud technologies and have extensive practice modeling data, querying, and deploying scalable pipelines to execute machine learning models.

You will collaborate with Data Scientists, ML Engineers, Data Engineers, and expert users to build and deploy scalable AI/ML solutions that drive value and insight from Micron’s manufacturing processes and systems. 

Responsibilities:  

• Design, build, and continuously refine ML models to address business challenges and enhance product capabilities.

• Analyze large datasets to uncover patterns, trends, and insights that inform and improve machine learning models.

• Stay ahead of advancements in AI/ML and integrate emerging techniques into the MLOps lifecycle.

• Build and maintain robust, scalable data pipelines and infrastructure to support model training and deployment.

• Collaborate on data preprocessing and feature engineering to improve input data quality and model performance.

• Design and optimize data architectures across cloud platforms (Snowflake, GCP, Azure) for AI/ML use cases.

• Develop custom applications and implement CI/CD pipelines to support efficient ML solution deployment.

• Deploy, evaluate, and monitor models in production, balancing performance with cost efficiency and enabling continuous improvement.

• Partner with Product and Engineering teams to define and implement Generative AI integration strategies and roadmaps.

• Communicate insights and collaborate multi-functionally, translating complex analytics into actionable recommendations for diverse collaborators.

• Drive the technical vision and lead end-to-end execution of complex, multi-functional AI/ML projects.

• Mentor and coach developing and senior team members, elevating the overall technical competence of the engineering team.

• Embrace and champion AI-assisted software development such as Opencode, Copilot; while strictly managing and auditing AI agents to ensure production quality code that is reliable, secure and high quality.

Minimum Qualifications:

• Master's or PhD degree in Computer Science, Machine Learning, Data Science, Statistics, or a field closely related to AI and Machine Learning with 8+ years (or PhD with 5+ years) experience building end-to-end ML systems on cloud platforms, automating model training, testing, and deployment.

• Strong experience with ML frameworks (scikit-learn, TensorFlow, PyTorch) and core techniques including regression, classification, deep learning, reinforcement learning, and generative AI.

• Proficient in Python or Java, with experience developing APIs and event-driven pipelines using Kafka, Pub/Sub, or similar messaging systems.

• Skilled in scalable data engineering, including ETL/ELT pipelines (Kubeflow, Airflow, Dataflow), SQL, and data architecture design.

• Hands-on experience with cloud and DevOps tools (GCP, AWS, Azure, Docker, Kubernetes), combined with strong analytical, communication, and collaboration skills.

• Proven track record of technical leadership, including leading ML project delivery and mentoring engineering teams. 

Preferred Qualifications:

• Strong foundation in machine learning and deep learning, with solid grounding in probability and statistics.

• Proven ability to productionize data science prototypes into scalable, real-world solutions.

• Advanced experience using and managing GenAI coding assistants, with a strong ability to peer-review and rigorously test AI-generated code logic.

• Hands-on experience building Generative AI solutions and intelligent agents (ADK, LangChain/LangGraph, CrewAI, DsPy, Semantic Kernel).

• Expertise in semantic search and retrieval systems, including RAG, GraphRAG, NLP, prompt engineering, and LLM fine-tuning/evaluation.