Risk Model Engineer - Pipeline Integrity (Transmission)

Irth Solutions · Remote

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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

Risk Model Engineer – Oil & Gas Transmission

Opportunity

If you want your work to make infrastructure smarter, safer, and more resilient, you’ll thrive here.

Irth Solutions builds advanced technology that empowers the people who keep our lights on, water running, and pipelines flowing. Our tools enhance the safety, reliability, and efficiency of critical infrastructure and the field technicians, engineers, and analysts who operate it.

We’re building a new AI-driven threat and risk management platform for pipeline asset integrity, and we’re looking for a Risk Model Engineer to own the transmission risk models that power its analytical layer.

Operators already run our probabilistic threat models in production today. You’ll migrate these models to our new platform, validate them against existing implementations, extend coverage across the transmission threat landscape, and help build Irth’s own model library. Your work will produce risk assessments and reliability calculations that pipeline operators can explain, defend, and support under regulatory scrutiny.

The work spans physics-based probabilistic models and machine learning, with a focus on producing results that are technically sound, explainable, and defensible.

Practical experience in North American oil and gas transmission is our top priority. We encourage strong industry candidates to apply even if their modeling experience is less extensive. We’re hiring two engineers and will balance the team’s strengths, with one potentially bringing deeper industry expertise and the other deeper modeling expertise.

Location: Remote within the United States or Canada, with working hours within approximately four hours of Central Time.

What You’ll Do

Migrate existing probabilistic model code to the new risk management platform and validate results against the current production implementation.
Integrate model inputs from diverse datasets across Irth’s product offerings.
Extend threat coverage across transmission pipeline integrity domains, including corrosion, cracking, third-party damage, outside forces, and other relevant threats.
Design and build time-series, anomaly detection, and geospatial models using inspection, sensor, maintenance, and related data.
Implement industry-standard probabilistic risk models within the analytical layer and support customer-driven model development on the platform.
Develop, evaluate, and present robust sensitivity analyses for all model offerings.
Validate model outputs against customer data, known outcomes, and applicable regulatory expectations.
Partner daily with ML Ops and data engineering teams, who own productionization, while you own the model methodology, content, validation, and supporting evidence.

What Will Help You Succeed

We don’t expect one candidate to bring every skill or experience listed. Practical North American oil and gas transmission experience is our highest priority. We’re looking for a combination of industry knowledge, quantitative modeling expertise, and sound engineering judgment across the two hires.

Transmission pipeline integrity: Experience with pipeline integrity management, threat identification, risk assessment, and integrity mitigation or remediation prioritization.
Threat and failure mechanisms: Familiarity with transmission pipeline threats such as external and internal corrosion, cracking, third-party damage, and other relevant threats.
Probabilistic risk modeling: Experience developing, implementing, migrating, or validating probabilistic models used to assess pipeline integrity or asset risk.
Inspection and operational data: Familiarity with in-line inspection (ILI), pipeline inspection records, sensor data, maintenance histories, or related operational datasets.
Reliability and sensitivity analysis: Experience evaluating model assumptions, uncertainty, sensitivity, and the impact of risk factors on model outputs.
Regulatory and customer-facing work: Experience supporting regulatory reviews, integrity assessments, audits, or customer decisions with clear, defensible technical evidence.
Machine learning and analytics: Experience applying machine learning to physical asset data, including time-series analysis, anomaly detection, classification, survival analysis, or related methods.
Geospatial analysis: Experience with spatial data, GIS tools, or geospatial risk assessment.
Python and software engineering: Sound engineering practices, including testing, code review, reproducibility, and disciplined use of AI coding assistants.
Real-world data and model judgment: Comfort working with incomplete or inconsistent data and the ability to distinguish a defensible, validated model from one that simply produces a number.

You don’t need to check every box. Candidates with deep transmission industry experience or strong quantitative modeling expertise are encouraged to apply, particularly if they bring working familiarity with the other area.

Why This Role Matters

Your work will help pipeline operators better understand threats, quantify risk, and make defensible integrity management decisions. You’ll contribute to the evolution of models already used in production while helping establish the analytical foundation of a new AI-driven platform.

You’ll work at the intersection of pipeline engineering, probabilistic risk, machine learning, and software development, with a direct impact on the safety and reliability of critical energy infrastructure.

Work Location

This is a remote position open to candidates based in the United States or Canada, with working hours that overlap within approximately four hours of Central Time.

Requirements

What Will Help You Succeed

We don’t expect a single candidate to bring all of the experience listed below. North American oil and gas transmission experience is our highest priority. We’re hiring two engineers and will balance the team’s strengths across industry expertise and quantitative modeling. Candidates with deep practical industry experience or strong risk modeling expertise are encouraged to apply, provided they have working familiarity with the other area.

Transmission pipeline integrity and regulation: Experience with North American oil and gas transmission integrity management, including applicable standards and regulations such as ASME B31.8S, 49 CFR 192, CSA Z662, or equivalents.
Fitness-for-service and failure mechanisms: Familiarity with fitness-for-service and remaining-strength assessments, corrosion growth modeling, or fracture mechanics.
In-line inspection (ILI) data: Experience working with ILI vendor data formats, data alignment, anomaly classification, and feature disposition.
Consequence and location analysis: Experience with high-consequence areas, class-location analysis, or related pipeline risk assessment requirements.
Regulatory and audit support: Experience preparing technical documentation and validation evidence for regulatory review, audits, or other formal assessments.
Quantitative risk and reliability modeling: Experience building or validating risk, reliability, or failure models for physical infrastructure. Pipeline experience is ideal, but relevant experience with rail, structural, water, or utility assets can transfer well.
Probabilistic methods and uncertainty: Familiarity with Monte Carlo simulation, structural reliability, uncertainty quantification, or related probabilistic methods.
Machine learning and physical asset data: Experience applying machine learning to time-series, survival, anomaly detection, or classification problems involving physical asset data. Experience training and validating models on sparse, imbalanced failure events is particularly valuable. We’re especially interested in candidates who are strong in either machine learning or pipeline integrity and have working familiarity with the other.
Geospatial analysis: Experience with ArcGIS, PostGIS, linear referencing, or geospatial risk assessment.
Python and software engineering: Sound engineering practices, including testing, code review, reproducibility, and disciplined use of AI coding assistants.
Real-world data and model judgment: Comfort working with incomplete, inconsistent, and imperfect data, along with the judgment to distinguish a defensible, validated model from one that simply produces a number.

You don’t need to check every box. We value sound technical judgment, practical industry knowledge, and the ability to build or validate models whose results can be explained and defended. We’re particularly interested in candidates who bring deep transmission expertise or strong quantitative modeling skills and can collaborate effectively across both domains.

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.