Machine Learning Engineer

Verifran; Canada; Hybrid; CAD 100,000 - 140,000; Full time

Job summary

Verifran is seeking a Senior ML Engineer to own the FRS and FCI models end to end, from feature design to deployment. You will ensure calibration and drift monitoring, and make every dimension explainable to stakeholders who rely on the score.

You will audit for proxy bias, design evaluation tests upfront, and collaborate with domain rules to keep the score fair and trustworthy. This is a remote role with flexible overlap requirements.

Qualifications

• Shipped a model into production and lived with its failures.
• Able to explain a prediction to non-technical stakeholders.
• Strong evaluation mindset with upfront test design.
• Comfortable that the honest answer may be: we cannot score that.

Responsibilities

• Own the FRS and FCI models end to end — features, training, evaluation, deployment.
• Build the calibration and drift monitoring that proves a score still means what it meant.
• Make every dimension explainable: show the reason behind the score.
• Audit for proxy bias and design evaluation to catch it.
• Collaborate with domain rules; ensure score remains within consent.

Skills

Applied ML

Model deployment

Explainability

Evaluation design

Bias auditing

Tools

Python

PyTorch

FastAPI

Job description

Own the models the whole company is named after: the Franchise Readiness Score, the Compatibility Index, and the calibration that keeps both honest.

TeamIntelligence TypeFull-time LocationRemote — anywhere with 4 hours overlap with Eastern Time

The FRS is a number people make life-changing decisions on. That makes calibration a product requirement rather than a metric: a 78 has to mean the same thing this quarter as it did last, and has to mean the same thing for two franchisees who are not alike.

The interesting problem is not accuracy, it is fairness and drift. The score must not learn proxies for things it has no business scoring, and it must stay stable as the population it sees changes. You will spend as much time on evaluation and explanation as on modelling.

What you will do

• Own the FRS and FCI models end to end — features, training, evaluation, deployment
• Build the calibration and drift monitoring that proves a score still means what it meant
• Make every dimension explainable: a franchisee is owed the reason, not just the number
• Audit for proxy bias, and design the evaluation that would catch it
• Work with the domain rules rather than around them — the score lives inside consent

What we are looking for

• Solid applied ML, and judgement about when a model is the wrong answer
• You have shipped a model into production and lived with its failures
• You can explain a prediction to someone it affects, in their language
• Rigorous about evaluation — you design the test before you like the result
• Comfortable that the honest answer is sometimes 'we cannot score that'

Nice to have

• Credit scoring, underwriting, admissions, or another consequential-decision domain
• Fairness and explainability work you can point at
• Experience calibrating a score that non-technical people act on

The stack

• Python
• PyTorch
• FastAPI

Remote

This role is fully remote. We care about overlap, not postcode — four hours with Eastern Time is enough.

How we hire

A conversation, a piece of real work paid at our rate, and a call with the team you would join. No whiteboard puzzles, no unpaid take-homes.