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Jump! Recruiters

Applied Machine Learning Scientist

Toronto, ON, Canada
Greater Toronto Area, Canada, Canada

Full-time

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Job details

Job type: Full-time
Apply by: Nov 28, 2026
Experience: 3 to 5 years of experience with MLOps and ML lifecycle management, including model monitoring, drift detection, model versioning, feature stores, and automated retraining.
Posting language: English
Posted: Oct 29, 2026

Original job description

Our client, Velan, is a global leader in the design and manufacture of industrial valves. Founded in Montreal, the company is recognized for the quality of its products, its technical expertise, and its human and collaborative corporate culture.

They are currently looking at adding an Applied Machine Learning Scientist, who will develop, deploy, and continuously improve scalable AI/ML solutions that solve meaningful business problems and improve decision-making, workflow efficiency, and process automation across the organization. Working at the intersection of data science, software engineering, and MLOps, you will transform prototypes into reliable production systems, ensure models remain accurate and trustworthy over time, and collaborate with cross-functional stakeholders to translate business needs into practical, high-quality technical solutions.

What Velan Offers

In addition to meaningful work that matches your ambitions and provides diverse learning opportunities, joining Velan means becoming part of a passionate team and enjoying exceptional working conditions:

Competitive salary and bonus program
Comprehensive group insurance program
24/7 telemedicine service
Employer contribution of 5% to a retirement savings plan
Competitive vacation policy
Paid holiday shutdown during the Christmas season
Reimbursement of professional association membership fees
Tuition reimbursement program
Annual gym membership reimbursement and other wellness initiatives
Free on-site parking
Company events (health, family, sports, etc.)
Fast-track career growth and professional development opportunities

Responsibilities

Design, develop, and deploy machine learning models that address high-value business problems and support scalable process automation.
Build and maintain end-to-end ML pipelines, including data ingestion, feature engineering, training, evaluation, deployment, inference, and monitoring.
Convert proof-of-concept models into production-grade solutions that are scalable, robust, performant, and reliable across changing data conditions.
Support the development of autonomous and multi-agent systems that can reason, plan, execute tasks, and integrate with enterprise tools, databases, APIs, and services.
Monitor deployed models for performance degradation, data drift, bias, and fairness, and retrain or recalibrate models when needed.
Partner with domain experts, engineers, and business stakeholders to define requirements, frame problems, translate objectives into technical solutions, and communicate results clearly.
Apply software engineering best practices, including version control, code review, automated testing, CI/CD, containerization, and orchestration.
Contribute to ML system architecture and technology decisions, ensuring alignment with the company’s data platforms, infrastructure, and technology standards.
Promote strong data and model governance through clear documentation, reproducibility, explainability, and auditability of ML solutions.

Qualifications

Bachelor’s degree in Computer Science, Mathematics, Software Engineering, Data Science, or a related technical field.
3 to 5 years of experience with MLOps and ML lifecycle management, including model monitoring, drift detection, model versioning, feature stores, and automated retraining.
Strong programming proficiency in Python and R, with familiarity with additional languages such as Java or C++.
Hands-on experience with machine learning frameworks and libraries such as PyTorch, TensorFlow, and scikit-learn.
Solid understanding of machine learning and deep learning fundamentals, including common architectures, training approaches, and evaluation methods.
Strong software engineering fundamentals, including data structures, algorithms, complexity, system design, version control, testing, and modular code development.
Experience working with data using SQL or other query languages, including large-scale data manipulation, preprocessing, and feature engineering.
Excellent communication and collaboration skills, with the ability to explain technical concepts to non-technical stakeholders and work effectively across functions.

Preferred Qualifications

Experience deploying ML solutions on cloud platforms such as Azure, AWS, or GCP, using containerization and orchestration tools such as Docker and Kubernetes.
Familiarity with emerging AI/ML domains such as large language models, computer vision, reinforcement learning, graph machine learning, or agentic systems.
Demonstrated experience with model explainability, fairness assessment, and bias detection or mitigation.
Evidence of strong interest in AI/ML through open-source contributions, publications, applied projects, competitions, or continuous learning.

If you recognize yourself in this profile and are excited about this opportunity, we would be delighted to learn more about you!

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More about this job

Responsibilities

Design, deploy, and maintain scalable machine learning models and end-to-end pipelines, turning prototypes into reliable production systems. Monitor model performance and governance, support autonomous and multi-agent systems, and collaborate with stakeholders to translate business needs into practical technical solutions.

Requirements

A bachelor’s degree in a relevant technical field and 3–5 years of experience with MLOps and machine learning lifecycle management are required. Candidates should have strong Python and R skills, experience with machine learning frameworks and data processing, solid software engineering fundamentals, and the ability to communicate and collaborate across functions.

Skills

Machine Learning
MLOps
Python
R
PyTorch
TensorFlow
Scikit-Learn
SQL
Model Monitoring
Feature Engineering
Model Deployment
Data Drift Detection
Model Explainability
Docker
Kubernetes

Benefits

Competitive Salary and Bonus Program
Comprehensive Group Insurance
24/7 Telemedicine Service
Competitive Vacation Policy
Paid Christmas Holiday Shutdown
Professional Association Membership Fee Reimbursement
Tuition Reimbursement
Annual Gym Membership Reimbursement
Wellness Initiatives
Free On-Site Parking
Company Events
Career Growth and Professional Development Opportunities

Visa sponsorship

Not detected in the job text

Keywords

Machine Learning
MLOps
Artificial Intelligence
Python
R
Java
C++
PyTorch
TensorFlow
Scikit-Learn
SQL
Machine Learning Pipelines
Feature Engineering
Model Monitoring
Data Drift
Model Versioning
Feature Stores
Automated Retraining
Deep Learning
Autonomous Systems
Multi-Agent Systems
Large Language Models
Computer Vision
Reinforcement Learning
Graph Machine Learning
Azure
AWS
Google Cloud Platform
Docker
Kubernetes

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