Forward Deployed Engineer building production AI systems for Sperton’s operating businesses. Discovering high - impact workflows, shipping end - to - end software, and measuring operational impact.
About the role
- Risk Model Engineer developing validated, explainable gas distribution risk models for Irth’s AI-driven pipeline integrity platform. Defining threats, integrating asset data, and supporting utility replacement prioritization.
Responsibilities
- Own gas distribution risk models within Irth’s analytical layer
- Help define gas distribution threat frameworks and model sets with subject-matter experts and design-partner operators
- Build risk models and take them through validation
- Produce explainable, calibrated, and defensible risk rankings for utility replacement programs and regulatory proceedings
- Own model content, including what the model computes, why it computes it, and the evidence supporting its validity
- Translate the gas distribution threat framework into systematic threat identification methods and supporting data structures
- Integrate model inputs from diverse datasets across Irth’s product offerings
- Identify gaps, inconsistencies, and limitations in source data and determine how they should be reflected in model inputs and outputs
- Extend threat coverage across legacy material corrosion, plastic embrittlement, excavation damage, cross-bores, and other gas distribution threats
- Collaborate closely with dedicated ML Ops and data engineering roles responsible for model serving, data pipelines, deployment, and monitoring infrastructure
Requirements
- Relevant experience in gas distribution integrity management, including program design, threat identification under 49 CFR Part 192, Subpart P, and replacement prioritization
- Experience with legacy material risk, including cast iron, bare steel, vintage plastics, and plastic embrittlement
- Experience with leak survey, methane detection, and cross-bore programs
- Machine learning applied to physical asset data, including time-series, survival, anomaly-detection, and classification models
- Strong depth in one or more relevant capability areas
- Curiosity and judgment to work across other capability areas
- Ability to collaborate with subject-matter experts, ML Ops, and data engineering roles
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
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
No Education Requirement
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