Senior Machine Learning Engineer
New
I
InsightMachine learning
Any City/Province in Canada; You will have the flexibility to work fully remotely.Full-TimeSenior
SalaryThe base salary range for this position is typically $140,000 CAD to $160,000 CAD with additional bonus and benefits available.
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Job Details
- Experience
- 5+ years applied ML engineering, with at least 2 years productionizing models at scale.
- Required Skills
- PythonNumpyPandasscikit-learn
Requirements
- Have 5+ years of applied ML engineering experience, including at least 2 years productionizing models at scale.
- Have shipped models used by real users or downstream systems.
- Bring deep experience with GEAP Model Registry, Pipelines (KFP), Experiments, Feature Store, and Workbench.
- Be able to stand up a GEAP project from zero.
- Have experience with regression and anomaly detection.
- Know gradient boosting methods such as XGBoost, LightGBM, or CatBoost.
- Know classical statistical anomaly methods such as IQR, isolation forests, or robust z-scores, and at least one deep approach such as autoencoders or normalizing flows.
- Use empirical results to justify architecture choices.
- Write production Python that is type-annotated, tested, and packaged.
- Be comfortable with pandas, NumPy, scikit-learn, and the GEAP SDK.
- Be familiar with KFP DSL for pipeline authoring.
Responsibilities
- Build anomaly detection and regression models.
- Develop end-to-end pipelines for data quality checks, feature engineering, model training, tuning, evaluation, and batch or online inference.
- Migrate legacy ML workloads from platforms such as Dataiku or SageMaker to KFP-based pipeline templates on GEAP.
- Implement input and output distribution-shift detection and accuracy regression checks.
- Define per-model thresholds and retraining cadence, and configure alerts and retraining decisions.
- Ensure retraining deploys through the standard CD pipeline with canary, approval gate, and automated rollback.
- Register models in GEAP Model Registry with model cards covering ownership, lineage, evaluation results, and lifecycle state.
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