Model Engineer

Job Description:

• Migrate existing probabilistic model code to the risk management platform
• Validate migrated results against the current implementation
• Integrate model inputs from diverse datasets across Irth’s product offerings
• Extend threat coverage across corrosion, cracking, third-party damage, outside forces, and other transmission pipeline threats
• Design and build time-series, anomaly detection, and geospatial models for inspection, sensor, and maintenance data
• Implement Irth’s own model library alongside the existing model suite
• Produce explainable and defensible risk outputs for pipeline operators and regulators
• Own model content, including computations, rationale, and evidence demonstrating correctness
• Work closely with ML Ops and data engineering teams responsible for serving, pipelines, deployment, and monitoring infrastructure
• Collaborate with data engineering, ML Ops, data science, and subject-matter experts from concept through validated implementation
• Support validated parity of existing models on the new platform
• Implement and support customer-built models on the platform
• Ensure model outputs are reproducible and traceable

Requirements:

• Experience building or validating quantitative risk, reliability, or failure models for physical infrastructure
• Pipeline experience is ideal; experience with rail, structural, water, electric, or other utility assets transfers well
• Familiarity with pipeline integrity standards and regulations, such as ASME B31.8S, 49 CFR 192, CSA Z662, or equivalent frameworks
• Experience with fitness-for-service and remaining-strength assessment, corrosion growth modeling, or fracture mechanics
• Experience working with inline inspection (ILI) data, including vendor formats, data alignment, anomaly classification, and feature disposition
• Experience with Monte Carlo simulation, structural reliability, and uncertainty quantification
• Experience applying machine learning to physical asset data, including time-series, survival, anomaly detection, and classification models trained and validated against sparse or imbalanced events
• Experience with consequence-area and class-location analysis
• Geospatial analysis experience, including ArcGIS, PostGIS, or linear referencing
• Experience producing model validation evidence suitable for regulatory, audit, customer, or other formal review
• Strong software engineering practices, including testing, code review, reproducibility, and disciplined use of AI-assisted development tools
• Deep expertise in one or more listed capability areas, with ability to learn and collaborate across others

Benefits:

• Competitive compensation package based on experience and qualifications
• Medical, Dental, and Vision Insurance
• 401(k) Plan with Company Match
• Generous Paid Time Off (PTO)
• Company-Paid Holidays
• Flexible Work Options
• Work-from-home opportunities, depending on role and business needs
• On-Call Compensation for eligible on-call shifts