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Risk Model Engineer — Pipeline Integrity (Transmission)
Location: Remote (US or Canada)
Department: Insights (AI/ML)
Reports to: Data Science Team Lead
About the Role
Irth is building a new AI-driven threat and risk management platform for pipeline asset integrity. The platform brings together three capabilities that have never previously lived in one place at Irth :
A governed, cross-product data platform built on Databricks and Azure.
An AI-powered ingestion layer that normalizes, repairs, and enriches customer data without services-heavy onboarding.
A reusable analytical layer that runs industry-standard, Irth -developed, and customer-built risk models against that data.
We are hiring a Risk Model Engineer to own the transmission risk models within this analytical layer.
Operators already run our probabilistic threat models in production inside the application. In this role, you will translate those models to the new platform, extend coverage across the transmission threat set, and build Irth ’s own model library alongside our existing model suite.
Your work will produce risk outputs that pipeline operators need to understand, defend, and stand behind with regulators. The role spans physics-based probabilistic models and machine learning, feeding reliability calculations that remain explainable and defensible.
You will own the model content : what it computes, why it computes it, and the evidence that demonstrates it is correct.
Productionization—including serving, pipelines, deployment, and monitoring infrastructure—is owned by dedicated ML Ops and data engineering teams that you will work with closely every day.
Key Responsibilities
Model Migration & Integration
Migrate existing probabilistic model code to the risk management platform and validate results against the current implementation.
Integrate model inputs from diverse datasets sourced across Irth ’s product offerings.
Model Development
Extend threat coverage across domains including:
Corrosion
Cracking
Third-party damage
Outside forces
Other transmission pipeline threats
Design and build time-series, anomaly detection, and geospatial models applied to inspection, sensor, and maintenance data.
Implement industry-standard probabilist
Requirements
Skills We’re Hiring For
We’re hiring for the capabilities below, but we do not expect a single candidate to have all of them . Deep expertise in one or more areas, combined with the ability to learn and collaborate across the others, is what matters.
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 tools and technologies such as 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.
Success Metrics
You’ll know you’re succeeding when:
Existing models are running on the new platform with validated parity against the current implementation.
New threat models take advantage of the integration of Irth datasets to produce meaningful analytical capabilities.
Customer-built models are successfully implemented, validated, and supported on the platform.
Validation evidence stands up to design-partner and regulatory review, with every model output reproducible and traceable.
You collaborate effectively with data engineering, ML Ops, data science, and subject-matter experts to move models from concept through validated implementation.
Location: Remote within the US or Canada Benefits
Benefits
Competitive Salary – A competitive compensation package based on experience and qualifications.
Medical, Dental, and Vision Insurance – Comprehensive insurance coverage to support you and your family.
401(k) Plan with Company Match.
Generous Paid Time Off (PTO) – Time off to support work-life balance and personal needs.
Company-Paid Holidays – Paid holidays throughout the year.
Flexible Work Options – Work-from-home opportunities are available, depending on role and business needs.
On-Call Compensation – Additional pay for eligible on-call shifts.