Senior Machine Learning Engineer
Actively Hiring
Full-time Posted 5 months ago
Responsibilities
- check_circle Analyze complex manufacturing datasets to uncover patterns and inform model design
- check_circle Build and iterate machine learning models to solve targeted business and engineering challenges
- check_circle Develop and maintain scalable data and solution pipelines supporting training and deployment
- check_circle Deploy, monitor, and optimize ML pipelines and models in cloud environments
- check_circle Find opportunities to integrate Generative AI into platforms and products
Basic qualifications
- Master’s degree in Computer Science, Machine Learning, Data Science, Statistics, or a related field (or equivalent experience)
- 2+ years building and deploying end‑to‑end machine learning systems in cloud platforms (GCP or snowflake or AWS)
- 2+ Hands-on experience with ML frameworks such as TensorFlow, PyTorch, or scikit‑learn
- 2+ Strong programming skills in Python or Java and proficiency in SQL
- 2+ Experience with Docker, Kubernetes, and event‑driven pipelines using systems like Pub/Sub, Solace, or Kafka, GKE
- 2+ Experience developing ETL/ELT pipelines with Kubeflow, Dataflow, and/or Airflow, and/or NIFI
- 2+ Knowledge of NLP, prompt engineering, model evaluation, and LLM fine‑tuning
- 2+ Working knowledge of databases such as BigQuery, Snowflake, MSSQL, or PostgreSQL
Preferred qualifications
- PhD or equivalent experience in a field related to AI or Machine Learning
- Experience building Generative AI solutions using frameworks such as ADK, LangChain/LangGraph, CrewAI, DsPy, or Semantic Kernel
- Experience developing RAG, GraphRAG, or semantic search systems
- Experience with computer vision, signal processing, or advanced ML algorithms
Tags & Focus Areas
Fulltime Ai Machine Learning Data Science
About Micron Technology
The Manufacturing Engineering (ME) role leverages advanced area knowledge to optimize area performance and reduce cycle time. The ME collaborates with shift operations and engineering teams to expedite product movement and react to process issues that could impact quality. Additional responsibilities include, but are not limited to: