TIMP Model Engineer

Cornerstone Global Partners (CGP Group); United States; Hybrid; USD 120,000 - 190,000; Full time

Benefits offered by this job

Competitive salary

Medical, dental, and vision insurance

401(k) plan with company match

Generous paid time off (PTO)

Company-paid holidays

Flexible work options

On-call compensation

Job summary

Cornerstone Global Partners (CGP Group) is seeking a Risk Model Engineer to own the transit risk models within a new AI-driven threat and risk platform for pipeline asset integrity. You will translate existing probabilistic models into the Databricks/Azure stack, extend coverage, and build a reusable model library.

You will work closely with ML Ops, data engineering, and domain experts to productionize models, provide validation evidence, and support regulators and customers with transparent

Qualifications

• Hands-on experience in pipeline integrity, operations, consultancy, or regulatory work.
• Familiarity with pipeline integrity standards and regulations (ASME B31.8S, 49 CFR 192, CSA Z662).
• Experience building or validating quantitative risk, reliability, or failure models for physical infrastructure.
• No single requirement is mandatory; strength in one area can offset gaps in others.

Responsibilities

• Migrate existing probabilistic model code to the risk management platform and validate results.
• Integrate model inputs from diverse datasets across our product offerings.
• Extend threat coverage across transmission pipeline threats (corrosion, cracking, third-party damage, etc.).
• Design and build time-series, anomaly detection, and geospatial models for inspection, sensor, and maintenance data.
• Implement industry-standard probabilistic models alongside in-house and customer-built models.
• Collaborate with ML Ops, data engineering, data science, and experts to move models from concept to implementation.
• Produce validation evidence suitable for regulatory, audit, customer, or formal review.

Job description

About Our Client

Our client is a growing SaaS company building critical infrastructure management software for energy, utilities, and telecom operators.

Role Overview

We're looking for a Risk Model Engineer to own the transmission risk models within a new AI-driven threat and risk management platform for pipeline asset integrity, which brings together a governed data platform on Databricks and Azure, an AI-powered ingestion layer, and a reusable analytical layer that runs industry-standard, in-house, and customer-built risk models. You'll translate existing probabilistic models already running in production to the new platform, extend threat coverage across the transmission threat set, and build out an in-house model library. The role spans physics-based probabilistic models and machine learning, producing risk outputs that pipeline operators need to understand, defend, and stand behind with regulators. Productionization (serving, pipelines, deployment, monitoring) is owned by dedicated ML Ops and data engineering teams you'll work with closely, while you own the model content itself: what it computes, why, and the evidence that it is correct. This is a domain-first role, at least one of the two hires needs real hands-on industry experience, with industry knowledge weighing more heavily than specific tooling. No single candidate is expected to check every box below, strength in one area can offset gaps in another.

Key Responsibilities

• 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 our product offerings
• Extend threat coverage across transmission pipeline threats, including corrosion, cracking, third-party damage, outside forces, and other threats
• Design and build time-series, anomaly detection, and geospatial models applied to inspection, sensor, and maintenance data
• Implement industry-standard probabilistic models alongside in-house and customer-built models
• Collaborate closely with ML Ops, data engineering, data science, and subject-matter experts to move models from concept through validated implementation
• Produce validation evidence suitable for regulatory, audit, customer, or other formal review

Requirements

• Real hands-on experience in pipeline integrity, operations, consultancy, or regulatory work (at least one of the two hires)
• Familiarity with pipeline integrity standards and regulations, such as ASME B31.8S, 49 CFR 192, or CSA Z662
• Experience building or validating quantitative risk, reliability, or failure models for physical infrastructure (pipeline experience ideal, but rail, structural, water, or electric utility experience transfers well)
• No single requirement above is an absolute must-have, strength in one area can offset less depth in another

Nice-to-Have

• Experience with fitness-for-service, 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 on sparse or imbalanced events
• Experience with consequence-area and class-location analysis
• Geospatial analysis experience (ArcGIS, PostGIS, linear referencing)
• Strong software engineering practices (testing, code review, reproducibility, disciplined use of AI-assisted development tools)
• Python, Databricks, and similar programming or tooling skills (secondary to hands-on domain experience)

Benefits

• Competitive salary 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, depending on role and business needs
• On-call compensation for eligible on-call shifts

As part of our hiring process, this role may use artificial intelligence or automated tools to assist with reviewing and screening applications. These tools support, but do not replace, human judgment in making hiring decisions.