Senior Machine Learning Engineer (Canada Only)

FreshBooks

About The Position

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. This role is ideal for someone who loves seeing ideas come to life, enjoys taking initiative, and is excited to deliver customer impact through data and ML.

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. Skillful at anticipating communication needs across the organization.
Demonstrated initiative, adaptability, and ability to operate independently on complex problems.

Nice To Haves

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.

Responsibilities

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. Skilfully adapt messaging to varied audiences; collaborating with cross-functional teams to facilitate alignment and proactively address communication gaps or misunderstandings.
Contribute to standards and documentation, mentor junior team members, and help shape our evolving ML platform. Regularly provide informal guidance, support peer collaboration, and influence task outcomes to ensure high-quality delivery.
Other duties may be assigned.