Senior Machine Learning Engineer – Toronto / Remote (Canada) | Visa + Salary Insights

📍 Location: ca

🏷 Type: Full-time

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Job Overview

The Senior Machine Learning Engineer role at FreshBooks focuses on building, deploying, and scaling production-grade ML systems that directly power product features and internal decision systems. This position sits at the intersection of applied machine learning and software engineering, requiring ownership of the full ML lifecycle—from experimentation and model prototyping to deployment, monitoring, and continuous improvement. The ideal candidate is a senior-level ML practitioner (5+ years) with strong Python, SQL, and production system design experience. You will collaborate closely with data engineering and product teams to deliver robust, scalable, and business-impacting ML solutions in a fast-moving SaaS environment.

📅 Job Timeline & Status

This role is in an active hiring phase with no visible closing date, indicating rolling review. Given the seniority level and breadth of responsibilities, competition is expected to be high. Candidates with strong production ML and LLM deployment experience should apply immediately due to high urgency and continuous applicant inflow.

🌍 Work Eligibility & Location

This is a Remote-first Canada-based role with optional office presence. Candidates outside Canada may face work authorization constraints due to missing sponsorship clarity.

💰 Salary Intelligence

The compensation is competitive for a SaaS-based senior ML engineering role in Canada, especially when factoring in equity grants and benefits. The range aligns with market expectations for production ML engineers with LLM and cloud infrastructure expertise.

📊 Role Breakdown

This role is heavily focused on building end-to-end machine learning systems in production environments. Approximately 40% of your time will involve designing and training ML models for classification, ranking, embeddings, and generative tasks. Around 30% is dedicated to production engineering, including deployment pipelines, CI/CD integration, and observability systems using tools like Docker, Kubernetes, and Airflow. Another 20% involves experimentation design such as A/B testing, statistical evaluation, and performance monitoring. The remaining 10% focuses on cross-functional collaboration, mentoring, and refining internal ML best practices. You will also work with GCP services like Vertex AI and BigQuery, ensuring scalable and reliable ML workflows that support both batch and real-time inference systems.

🧩 Required Skills & Fit

📈 Difficulty & Competitiveness

This is a technically demanding senior role requiring both strong theoretical ML foundations and practical engineering depth. Candidates lacking production deployment experience or cloud-based ML infrastructure exposure will likely face rejection early in the funnel.

🚀 Career Impact

This role significantly enhances long-term career mobility, particularly for engineers aiming to transition into staff-level ML engineering or AI systems architecture roles. Exposure to production LLM pipelines and cloud-scale ML infrastructure strengthens both technical depth and leadership trajectory.

📋 Key Responsibilities

You will be responsible for designing, building, and maintaining production-grade ML systems that power FreshBooks features. Core responsibilities include developing models for prediction, ranking, and embeddings using Python-based ML stacks, and deploying them into scalable environments using GCP, Docker, and orchestration tools like Airflow. You will implement monitoring systems to detect drift, performance degradation, and data quality issues. Additionally, you will collaborate with engineering teams to integrate ML outputs into real-time and batch systems while ensuring reliability, scalability, and reproducibility across pipelines.

🎯 Application Strategy

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🧠 Fit & Positioning Analysis

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📅 Application Signals

This role shows strong ongoing hiring momentum with rolling applications and no fixed deadline. Expect moderate-to-high competition due to FreshBooks’ brand recognition and remote flexibility. Interview cycles are likely to be structured and multi-stage, involving ML system design and coding assessments. Candidates with strong production ML experience should act quickly as early applicants typically receive faster screening. Since “Applications may close without notice.”, timely submission is critical.

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✅ Job Source & Verification

This listing is derived directly from FreshBooks’ official job posting environment. Details are consistent with standard SaaS industry hiring patterns for senior ML engineering roles. Last updated: 2026-06-07. The information reflects a high-confidence extraction of role requirements, compensation bands, and responsibilities typical of FreshBooks engineering teams.