Machine Learning Engineer II
Core
Design, build, and deploy next-generation predictive and prescriptive maintenance systems using industrial sensor data and PLCs to detect equipment failure signatures and prescribe optimized corrective actions.
Role type
Senior IC Machine Learning Engineer (Industrial IoT/Predictive Maintenance)
Builds
Production-grade predictive maintenance models, Agentic AI decision workflows, and automated MLOps pipelines for industrial assets.
Domain
Industrial IoT, Predictive Maintenance, Manufacturing Operations
Deliverable
production ML models
Required skills
PyTorch, TensorFlow, Python, C++, SQL, time-series databases, MLOps (MLflow, Kubeflow, Docker, Kubernetes), cloud architecture (Azure/GCP/AWS), supervised/unsupervised learning, anomaly detection, RAG systems
Preferred skills
Master's or PhD in quantitative field, 5-7 years experience in Python/ML frameworks, experience with LLMs and agentic applications
Technologies
Azure Machine Learning Studio, PyTorch, TensorFlow, Scikit-Learn, NumPy, Pandas, SciPy, XGBoost, Random Forests, LSTMs, Autoencoders, Isolation Forests, One-Class SVMs, Dynamic Time Warping, PCA, MLflow, Kubeflow, Docker, Kubernetes, PySpark, Databricks, InfluxDB, TimescaleDB
Responsibilities
Build robust classifiers for fault diagnosis and regression models for Remaining Useful Life (RUL) estimation; Develop multi-agent workflows that reason over asset health data and generate automated outputs; Standardize, clean, and enrich raw sensor telemetry and PLC tag data; Build and maintain scalable, reproducible training and inference pipelines; Deploy models across hybrid cloud and edge environments; Deliver highly optimized, production-grade modular software in Python and C++ with strict unit testing.
Seniority
Senior, hands-on IC