Micron is seeking an Engineer to join our team. In this role, you will design, develop, and deploy machine learning models and agentic workflows for predictive, prescriptive, and generative tasks. You will lead the end-to-end development from concept to production, establish best practices, and mentor junior engineers.
What You'll Do
- Design, develop, and deploy machine learning models for predictive, prescriptive, and generative tasks using advanced statistical and analytical techniques.
- Architect and lead the end-to-end development of ML and agentic workflows from concept to production.
- Build and operationalize agentic workflows capable of autonomous decision-making and adaptive learning, using frameworks like LangGraph, LangChain, CrewAI, and AutoGen.
- Define and implement production-grade agentic architectures with modularity, observability, and fault tolerance.
- Establish and promote Best Known Methods (BKMs) for deploying ML and agentic solutions, including prompt management, memory handling, and orchestration strategies.
- Continuously evaluate and fine-tune model and agent performance using structured evaluation frameworks, RLHF, and prompt tuning.
- Extract, cleanse, and analyze data from diverse sources using SQL, applying techniques for outlier detection and missing data handling.
- Collaborate with cross-functional teams to integrate ML and agentic solutions into scalable systems aligned with business goals.
- Mentor junior engineers, conduct code reviews, and contribute to technical design and architectural decisions.
- Lead the selection and implementation of ML and agentic tools, frameworks, and infrastructure for efficient development and deployment.
- Stay current with emerging trends in machine learning and agentic AI to drive innovation.
What We're Looking For
- 3-7 years of experience in data science, machine learning, or AI engineering roles.
- Proven track record of working on ML projects end-to-end and experience deploying ML models and agentic workflows in production.
- Prior contributions to technical design, architecture decisions, and mentoring junior engineers.
- Strong software development, analytical thinking, and problem-solving abilities.
- Excellent verbal, written communication, and collaboration skills.
Nice to Have
- Proven hands-on experience with cloud platforms, particularly Google Cloud Platform (GCP).
- Familiarity with cloud-native tools and infrastructure for model training, orchestration, and monitoring.
- Publications in top-tier conferences (CVPR, NIPS, ICML, KDD) are a plus.
Technical Stack
- Languages & Frameworks: Python (preferred), TensorFlow, PyTorch, Scikit-learn, JAX, LangChain, LangGraph, AutoGen, CrewAI, Google ADK
- ML Concepts: Supervised, unsupervised, reinforcement learning, deep learning, generative models
- Data Tools: SQL, PySpark, SparkR, SparklyR, outlier detection, missing data handling, Tableau or similar
- Infrastructure: Google Cloud Platform (GCP) or other cloud environments, MLFlow, Ray
- Specialized Tools: Hugging Face, OpenAI Gym, RLHF, prompt tuning, structured evaluation frameworks
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.
