Posted 10 June, 2026
Senior Machine Learning
NR Consulting
REMOTE,122002
Full Time
Reference: 365_463738_26-06171
Title: Senior Machine Learning
Location: REMOTE
Exp: 6+yrs
Job Description:
Required Qualifications:
• Experience: 5+ years of applied ML engineering experience, with at least 2 years productionizing models at scale.
• GCP & Vertex AI Ecosystem: Deep hands-on experience with Vertex AI Model Registry, Vertex AI Pipelines (KFP), Vertex AI Experiments, Vertex AI Feature Store, and Vertex AI Workbench.
• Core Modeling Chops: Strong regression and anomaly detection skills using gradient boosting (XGBoost, LightGBM, CatBoost), classical statistical methods (IQR, isolation forests, robust z-scores), and deep learning approaches (autoencoders, normalizing flows).
• Production Python: Mastery of type-annotated, tested, and packaged Python, alongside libraries like pandas, NumPy, scikit-learn, and the Vertex AI SDK.
• Pipeline Authoring: Familiarity with the KFP DSL for building ML pipelines.
• Data Engineering (BigQuery): Intermediate to advanced SQL skills, including window functions, partitioning, and query optimization, with the ability to read and debug complex BigQuery query plans.
• MLOps Fundamentals: Deep understanding of CI/CD for ML (GitHub Actions or equivalent), promotion gates, automated evaluation, drift detection, and batch vs. online evaluation workflows.
• Cross-Team Execution: Proven background working directly alongside data engineers, software engineers, and business SMEs, rather than working in an isolated research environment.
• Communication: Ability to articulate and defend modeling commitments to customer leadership, including VPs of engineering, data science leads, and domain experts.
Location: REMOTE
Exp: 6+yrs
Job Description:
Required Qualifications:
• Experience: 5+ years of applied ML engineering experience, with at least 2 years productionizing models at scale.
• GCP & Vertex AI Ecosystem: Deep hands-on experience with Vertex AI Model Registry, Vertex AI Pipelines (KFP), Vertex AI Experiments, Vertex AI Feature Store, and Vertex AI Workbench.
• Core Modeling Chops: Strong regression and anomaly detection skills using gradient boosting (XGBoost, LightGBM, CatBoost), classical statistical methods (IQR, isolation forests, robust z-scores), and deep learning approaches (autoencoders, normalizing flows).
• Production Python: Mastery of type-annotated, tested, and packaged Python, alongside libraries like pandas, NumPy, scikit-learn, and the Vertex AI SDK.
• Pipeline Authoring: Familiarity with the KFP DSL for building ML pipelines.
• Data Engineering (BigQuery): Intermediate to advanced SQL skills, including window functions, partitioning, and query optimization, with the ability to read and debug complex BigQuery query plans.
• MLOps Fundamentals: Deep understanding of CI/CD for ML (GitHub Actions or equivalent), promotion gates, automated evaluation, drift detection, and batch vs. online evaluation workflows.
• Cross-Team Execution: Proven background working directly alongside data engineers, software engineers, and business SMEs, rather than working in an isolated research environment.
• Communication: Ability to articulate and defend modeling commitments to customer leadership, including VPs of engineering, data science leads, and domain experts.
