

ChabezTech LLC
Machine Learning Engineer
β - Featured Role | Apply direct with Data Freelance Hub
This role is for a Machine Learning Engineer in Portland, OR, offering a 24-month contract at a competitive pay rate. Requires 5+ years of experience, strong Python skills, and expertise in machine learning, data analysis, and causal inference methods.
π - Country
United States
π± - Currency
Unknown
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π° - Day rate
Unknown
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ποΈ - Date
April 27, 2026
π - Duration
More than 6 months
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ποΈ - Location
On-site
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π - Contract
Unknown
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π - Security
Unknown
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π - Location detailed
Portland, OR
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π§ - Skills detailed
#AI (Artificial Intelligence) #Neural Networks #Programming #NumPy #PyTorch #ML (Machine Learning) #Data Science #Databases #HBase #Knowledge Graph #Datasets #Anomaly Detection #Data Analysis #TensorFlow #Monitoring #RDF (Resource Description Framework) #IoT (Internet of Things) #Graph Databases #Pandas #Security #Computer Science #Python #Neo4J
Role description
Job Description
Job Title: Machine Learning Engineer
Location: Portland, OR - Onsite (Local only / F2F interview)
Duration: 24 Months Contract
Experience Level: 5+ years of experience
Required Qualifications
- Bachelorβs or masterβs degree in computer science, Machine Learning, Electrical Engineering, or related field
- 5+ years of experience in machine learning, data science, or AI engineering
- Strong programming skills in Python (NumPy, Pandas, scikit-learn, PyTorch/TensorFlow)
- Experience with time-series data analysis and anomaly detection
- Hands-on experience with causal inference methods (e.g., Bayesian networks, structural causal models)
- Experience building or working with knowledge graphs (Neo4j, RDF, graph databases)
- Understanding of explainable AI techniques (SHAP, LIME, counterfactual analysis)
- Experience deploying ML models in production systems
- Strong problem-solving skills and ability to work with complex, real-world datasets
Preferred Qualifications
- Experience with fault tree analysis (FTA), reliability engineering, or failure analysis
- Background in industrial systems, semiconductors, manufacturing, or IoT environments
- Experience with graph-based ML / Graph Neural Networks (GNNs)
- Familiarity with RCA methodologies (FMEA, 5 Whys, fishbone diagrams)
- Experience with vector databases, RAG systems, or LLM-based reasoning
- Knowledge of MLOps practices (CI/CD, monitoring, model governance)
- Experience working in air-gapped or high-security environments
Additional Information
All your information will be kept confidential according to EEO guidelines.
Job Description
Job Title: Machine Learning Engineer
Location: Portland, OR - Onsite (Local only / F2F interview)
Duration: 24 Months Contract
Experience Level: 5+ years of experience
Required Qualifications
- Bachelorβs or masterβs degree in computer science, Machine Learning, Electrical Engineering, or related field
- 5+ years of experience in machine learning, data science, or AI engineering
- Strong programming skills in Python (NumPy, Pandas, scikit-learn, PyTorch/TensorFlow)
- Experience with time-series data analysis and anomaly detection
- Hands-on experience with causal inference methods (e.g., Bayesian networks, structural causal models)
- Experience building or working with knowledge graphs (Neo4j, RDF, graph databases)
- Understanding of explainable AI techniques (SHAP, LIME, counterfactual analysis)
- Experience deploying ML models in production systems
- Strong problem-solving skills and ability to work with complex, real-world datasets
Preferred Qualifications
- Experience with fault tree analysis (FTA), reliability engineering, or failure analysis
- Background in industrial systems, semiconductors, manufacturing, or IoT environments
- Experience with graph-based ML / Graph Neural Networks (GNNs)
- Familiarity with RCA methodologies (FMEA, 5 Whys, fishbone diagrams)
- Experience with vector databases, RAG systems, or LLM-based reasoning
- Knowledge of MLOps practices (CI/CD, monitoring, model governance)
- Experience working in air-gapped or high-security environments
Additional Information
All your information will be kept confidential according to EEO guidelines.





