Machine Learning Engineer II
Corp-Main - Diamond Bar, CA, United States
USD 100K-145K Entry-level Full Time
Tasks
- Build prescriptive decision workflows
- Build scalable training and inference pipelines
- Communicate model behavior to operations and IT stakeholders
- Deploy models to cloud and edge endpoints
- Design predictive maintenance systems
- Detect anomalies and predict remaining useful life
- Develop modular production software in Python and C++
- Develop supervised and unsupervised models for equipment health
- Engineer sensor and PLC data pipelines
- Implement automated MLOps pipelines
- Implement unit testing and documentation
- Integrate predictions with plant dashboards and CMMS workflows
- Monitor data drift and retrain models
- Optimize distributed training and throughput
Perks/Benefits
- N/A
Skills/Tech-stack
Agent systems | Agentic AI | Autoencoders | Azure Machine Learning | C++ | CI/CD | Cloud architectures | Databricks | Docker | Dynamic Time Warping | Edge Computing | InfluxDB | Isolation Forest | Kubeflow | Kubernetes | LLMOps | LSTM | Language Models | Large Language Models | MLOps | MLflow | Machine Learning | Multi-Agent | Multi-Agent Systems | NoSQL | NumPy | One-Class SVM | PCA | Pandas | PySpark | PyTorch | Python | R | RAG | Random Forests | Retrieval-Augmented Generation | SQL | SciPy | Scikit-learn | Series data | TensorFlow | Time Series | Time Series Data | TimescaleDB | XGBoost
Education
Regions
Countries
States
Cities
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