Applied Machine Learning Scientist in Montréal, Quebec at Velan Inc.

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.

As an Applied Machine Learning Scientist, you 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 We Offer

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 programComprehensive group insurance program24/7 telemedicine serviceEmployer contribution of 5% to a retirement savings planCompetitive vacation policyPaid holiday shutdown during the Christmas seasonReimbursement of professional association membership feesTuition reimbursement programAnnual gym membership reimbursement and other wellness initiativesFree on-site parkingCompany 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.