Applied ML Scientist

GuruLink; Montreal; On-site; CAD 80,000 - 110,000; Full time

Job summary

A leading company seeks a Machine Learning Scientist to transform product ideas into impactful machine learning solutions. This role involves defining problem statements, designing data strategies, and collaborating with engineering teams to produce production-ready features. Candidates should have a strong background in applied machine learning and Python.

Qualifications

• 5+ years in applied machine learning, preferably in fast-paced product environments.
• Deep Python fluency.
• Strong communication and documentation skills with cross-functional teams.

Responsibilities

• Translate product specs into clear ML problem statements.
• Design end-to-end data pipelines.
• Convert experimental prototypes into robust features.

Skills

Problem Definition & Metrics

Data Strategy

Prompt Engineering & ML Experimentation

Cross-Functional Collaboration

Scalability & Sustainability

Job description

Location: Montreal, Quebec

Are you an experienced Machine Learning Scientist passionate about turning ideas into impactful solutions?

We’re looking for someone who thrives at the intersection of product and research. In this role, you'll translate product ideas into machine learning problem statements, lead hands-on experimentation (including prompt engineering), and work closely with engineering teams to bring prototypes into production. You’ll have the opportunity to push the boundaries of what’s possible using state-of-the-art techniques in an environment that supports fast iteration, deep thinking, and practical impact.

The platform you’ll be contributing to empowers software engineers by automating tasks like code review, issue detection, and quality feedback—enabling dev teams to ship better software, faster.

Must Have Skills:

Problem Definition & Metrics
-Translate product specs into clear ML problem statements, including defining inputs, outputs, evaluation metrics, and success criteria.

Data Strategy
-Design end-to-end data pipelines: source, preprocess, annotate, QA, and maintain datasets needed for reliable ML development and evaluation.

Prompt Engineering & ML Experimentation
-Rapidly design and iterate on prompt-based models and other experimental components using modern frameworks and tooling.
-Analyze and refine results to continuously optimize model performance.

Prototype • Production
-Convert experimental prototypes into robust features.
-Build reproducible and versioned pipelines, and work with engineering teams to integrate into production environments (CI/CD ready).

Cross-Functional Collaboration
-Work hand-in-hand with product managers, software engineers, and ML operations to align on goals and integrate working models into production systems.

Scalability & Sustainability
-Contribute to ML best practices around modular design, versioning, testing, and documentation.
-Architect solutions with scalability and long-term maintainability in mind.

Nice to Have Skills:

-Proven ability to define ML problem statements from vague product requirements.
-Experience designing data strategies including sourcing, labeling, and QA practices.
-Hands-on expertise with prompt engineering, large language models, and ML experimentation frameworks.
-Experience turning prototypes into production-ready ML components.
-Strong communication and documentation skills with cross-functional teams.
-Nice-to-Have
-5+ years in applied machine learning, preferably in fast-paced product environments.
-Deep Python fluency.
-Experience working on developer tools or code-focused ML systems is a bonus.