Machine Learning Engineer
About The Position
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 • Bachelor’s or master’s 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.
Requirements
- Bachelor’s or master’s 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
Nice To Haves
- 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
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What This Job Offers
Career Level
Mid Level
Number of Employees
1-10 employees
