Applied ML Engineer - Model R&D

New

JobgetherAI/ML, financial markets

Based in Canada, Overlap with U.S. business hoursFull-TimeMiddle

SalaryAttractive total compensation package with significant performance-based bonus potential.

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Job Details

Professional experience in a structured software engineering environment with collaborative teams and shared system ownership.
Strong software engineering and programming skills, including experience building, maintaining, and deploying production-grade applications and systems.
Ability to turn complex or experimental technical work into reliable, maintainable, and scalable production solutions.
Understanding of software architecture, engineering practices, version control, code reviews, testing, and collaborative development workflows.
Experience with task-management tools such as Jira or Linear, Git-based workflows, and CI/CD practices.
Familiarity or practical exposure to model training, inference pipelines, or collaboration with ML research teams.
Ability to work independently, navigate ambiguity, and make sound technical decisions.
Willingness to focus primarily on applied engineering and deployment while remaining engaged with research and experimentation.
Ability to overlap with U.S. business hours while working remotely from Canada.
Proficiency in Rust or willingness and ability to develop expertise quickly.

Transform research-grade machine-learning code and prototypes into maintainable, scalable production systems.
Own ML deployment pipelines spanning data ingestion, feature engineering, model integration, inference, serving, monitoring, and reliability.
Translate research objectives and technical concepts into robust production solutions with researchers and engineering teams.
Strengthen software development practices, including task management, Git workflows, code reviews, testing, and CI/CD.
Identify architectural weaknesses, technical debt, and engineering quality issues, then implement improvements.
Build and maintain monitoring, logging, alerting, and observability systems.
Support the adoption, integration, and development of Rust-based infrastructure.
Participate in applied ML research and experimentation and translate relevant findings into engineering solutions.

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