Senior Machine Learning Engineer Job at FreshBooks

Senior Machine Learning Engineer

FreshBooks

Location:
Canada

Category:
IT - Software Development

Contract Type:
Not provided

Salary:

128000.00 - 160000.00 CAD / Year

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Job Description:

As a Senior Machine Learning Engineer, you will take end-to-end ownership of the machine learning lifecycle from early experimentation and model prototyping to deployment and monitoring in production. You handle varied and moderately complex challenges, moving beyond execution to refine processes and mentor others. You will be part of a growing team delivering ML-powered features, optimizing systems for reliability and performance, and supporting rapid experimentation with clear, measurable impact. This role is centered on machine learning, with supporting skills in software engineering to enable model development and deployment. You’ll be responsible for not only building models, but also packaging them for production and collaborating with our Data Engineering and Product teams on the design of APIs and infrastructure.

Job Responsibility:

Design, prototype, and validate machine learning models to power product features or internal tools
Own and lead all phases of the ML lifecycle from experimentation through to production deployment and model monitoring
Collaborate with Data Engineers and Product Engineers to integrate models into production infrastructure (batch and online serving)
Develop and prototype features for the shared feature store, including documentation, versioning, and consistency validation
Author high-quality, production-ready code with appropriate tests, observability, and monitoring hooks
Design experiments (e.g. A/B tests, pre-post analyses) and interpret results to guide product and business decisions
Design and build end-to-end pipelines for classification, ranking, embeddings, or generation tasks
Drive reliability practices in deployed models, including retraining logic, alerting on drift, and root cause analysis
Work closely with product and engineering stakeholders to align ML work with business priorities
Contribute to standards and documentation, mentor junior team members, and help shape our evolving ML platform

Requirements:

5+ years of experience in data science, applied ML, or ML engineering roles
Strong background in supervised and unsupervised learning, statistical modeling, and experimentation techniques
Proven experience developing and shipping ML models in production environments (batch or real-time)
Strong Python and SQL skills
comfort working with structured and unstructured data
Hands-on experience building and deploying ML or LLM-based systems (e.g. retrieval-augmented generation, embeddings, prompt tuning)
Familiarity with cloud infrastructure and ML tools, ideally on Google Cloud Platform (e.g. Vertex AI, BigQuery, Cloud Composer, Kubernetes)
Experience working with CI/CD pipelines, containerization (Docker), and job orchestration tools (Airflow, dbt, etc.)
Deep understanding of end-to-end ML operations including model observability, model drift detection, and model performance optimization
Strong communication skills and ability to explain technical concepts to non-technical stakeholders
Demonstrated initiative, adaptability, and ability to operate independently on complex problems

Nice to have:

Experience working with Agentic Models
Familiarity with LLM orchestration frameworks (e.g. ADK, LangChain, Semantic Kernel, Haystack)
A background in software engineering (e.g., system design, API development, or distributed systems), enabling strong collaboration with infrastructure teams and greater autonomy in full-stack ML delivery

Additional Information:

Job Posted:
February 19, 2026

Employment Type:

Fulltime

Work Type:

Remote work

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