TIMP Model Engineer

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.

Your application will only be counted once you complete the full registration process on the KeyStone platform, including creating your profile, uploading your CV, and submitting your application. The AI interview is optional and encouraged, but is not required for your application to be counted.