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Staff/Principal Full-Stack AI Engineer, IPAI, SMAI
Job Summary
Lead the Future of AI-Powered Manufacturing at Micron
Micron is accelerating its transformation toward Autonomous Operations through Artificial Intelligence, advanced analytics, digital twins, robotics, and intelligent automation. As a Staff/Principal Full-Stack AI Engineer, you will be a technical leader driving the strategy, architecture, and delivery of next-generation AI products that power smarter manufacturing across Micron's global network of fabs and assembly/test sites.
In this highly influential role, you will own AI solutions end-to-end—from data engineering, machine learning, and agentic AI systems to modern full-stack applications, digital twin platforms, robotics integration, and edge-to-cloud deployment. Working closely with data scientists, software engineers, robotics teams, and manufacturing leaders, you will transform innovative ideas into scalable, production-grade solutions that improve efficiency, increase automation, and enable intelligent decision-making at enterprise scale.
This is a unique opportunity to shape the future of AI in semiconductor manufacturing and directly contribute to Micron's vision of autonomous, data-driven operations worldwide.
Main Responsibilities
Design, architect, and build end-to-end AI products, including data ingestion pipelines, feature engineering, model training and inference, APIs, web applications, and observability frameworks.
Develop responsive front-end applications using React, Angular, or Streamlit, backed by Python/FastAPI services, with strong type-safe development and test coverage practices.
Architect and maintain scalable, modular application layers that bridge real-time sensor, robotics, and manufacturing data with enterprise-facing dashboards and decision-support systems.
Design and develop full-stack integration of robotics platforms, Autonomous Mobile Robots (AMRs), tool automation systems, and control systems with smart manufacturing applications and the Micron smart manufacturing ecosystem.
Design and implement digital twin environments, including 3D representations of fab layouts, AMR fleets, and tool stations using platforms such as NVIDIA Omniverse, Gazebo, Unity Robotics Hub, or equivalent technologies.
Interface with Smart Manufacturing systems to integrate structured operational data into AI pipelines and digital twin environments.
Design and integrate LLMs and agentic AI frameworks into production workflows for manufacturing and engineering users.
Develop AI-assisted robotics orchestration capabilities and 3D-aware AI features that leverage spatial data, equipment positioning, robot trajectories, and fab maps to improve anomaly detection, route optimization, and predictive maintenance.
Containerize AI and robotics services using Docker and deploy solutions through Kubernetes, OpenShift, and modern cloud-native platforms.
Package and deploy robotics software stacks, AI services, and 3D rendering workloads as containerized microservices supporting cloud, on-premises, and edge manufacturing environments.
Manage GPU-accelerated inference workloads and real-time simulation environments supporting AI, robotics, and digital twin applications.
Optimize AI application performance and cost through caching, batching, GPU utilization, model quantization, and inference optimization strategies.
Validate digital twin and simulation scenarios against real-world operational constraints before production deployment to support safe and reliable autonomous operations.
Other Responsibilities
Lead implementation of CI/CD, automated software releases, monitoring, model evaluation, drift detection, and AI application lifecycle management for production AI applications.
Apply Responsible AI practices, including input validation, prompt-injection defense, PII/IP protection, model governance, auditability, and compliance requirements.
Design and implement improvements to AI platform scalability, reliability, maintainability, performance, and cost efficiency.
Develop and communicate descriptive, diagnostic, predictive, and prescriptive analytics to support manufacturing and operational decision-making.
Develop, implement, and optimize machine learning, deep learning, and statistical models for structured and unstructured data.
Perform code reviews, provide technical mentorship, and drive software engineering best practices across project teams.
Lead testing, debugging, technical documentation, installation procedures, and maintenance strategies for AI and software solutions.
Integrate AI-assisted tools and insights into daily work to improve efficiency, quality, and effectiveness while complying with organizational standards, legal requirements, and governance policies.
Contribute to a culture of continuous improvement by identifying, testing, and sharing AI-enabled enhancements within one's scope of work.
Minimum Required Qualifications / Experience
Bachelor's or Master's degree in Computer Science, Software Engineering, Data Science, or a related field; equivalent industry experience accepted.
8+ years building and shipping production web, software, machine learning, or AI applications end-to-end.
Track record of delivering solutions in complex cross-functional environments spanning software engineering, data science, robotics, manufacturing, automation, and infrastructure teams.
Experience designing and deploying scalable AI applications, APIs, and data-driven solutions in production environments.
Must Have Technical Skills
Expert programming skills in Python and modern JavaScript/TypeScript, with experience designing scalable software architectures.
Strong SQL proficiency and experience designing data ingestion and feature engineering pipelines for AI/ML workloads at scale.
Experience with Docker, Kubernetes/OpenShift, cloud platforms (AWS, Azure, or GCP), and CI/CD pipeline ownership.
Experience with machine learning, deep learning, statistical modeling, predictive analytics, and large-scale computing frameworks.
Strong knowledge of GenAI technologies, including prompt engineering, function/tool calling, Retrieval-Augmented Generation (RAG), LLM integration, agentic AI patterns, model evaluation, and Responsible AI practices.
Experience working with distributed data processing platforms, enterprise AI systems, and production AI operations.
Ability to apply AI literacy and digital fluency to use AI-enabled tools responsibly and effectively for research, analysis, content creation, problem-solving, operational tasks, automation, and achieving business outcomes.
Must Have Soft Skills
Strong analytical and problem-solving skills.
Excellent communication and stakeholder management skills.
Ability to influence technical decisions across cross-functional teams.
Strong collaboration, ownership, and execution mindset.
Highly Desirable / Preferred Skills or Experience:
Master’s degree in Computer Science, Artificial Intelligence, Robotics, Data Science, or a related field, or equivalent experience.
Prior experience in semiconductor manufacturing, advanced manufacturing, industrial automation, digital twin development, or other high-reliability operational environments.
Experience implementing Generative AI solutions, including RAG architectures, prompt engineering, function/tool calling, agentic workflows, and LLMOps practices.
Experience applying Responsible AI, security, audit logging, and model governance practices within production applications.
Knowledge of GPU-accelerated computing, model optimization techniques, and deployment of AI workloads in cloud and edge environments.
Hands-on experience with digital twin and simulation platforms (NVIDIA Omniverse, Gazebo, Unity Robotics Hub, Siemens Tecnomatix, Unreal Engine) and the Autodesk stack (Revit, Navisworks, ACC, ReCap), including point-cloud processing, photogrammetry, Gaussian splatting, and geometry optimisation via IFC and Revit APIs.
Experience integrating robotics, autonomous mobile robots (AMRs), industrial automation, and IoT sensor networks into software applications, with proven robotics development in ROS/ROS 2 and Isaac Sim/Lab — including SLAM perception, LiDAR/depth-camera data processing, and sim-to-real transfer (imitation learning, domain randomization).
Proven experience in integration of SCADA/FMCS/BMS systems for smart facilitie