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

📍 Location: ca

🏷 Type: Not specified

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Table of Contents

Job Overview

ExaCare AI is hiring a Machine Learning Engineer to own the full lifecycle of production-grade AI systems in the healthtech domain. This is a mid-to-senior level (3+ years) role focused on building scalable ML solutions, optimizing large models, and deploying real-world applications. The ideal candidate combines deep technical expertise with strong experimentation discipline, especially in LLMs, MLOps, and data-centric AI, and thrives in fast-paced, high-impact environments.

🌍 Work Eligibility & Location

🌍 Visa Sponsorship: Not explicitly mentioned
✈️ Relocation Support: Not specified
🏠 Remote Type: Remote (Canada)
⏰ Timezone Requirement: Likely North America alignment
🌐 Country Restrictions: Must be eligible to work in Canada
🗣️ Language Requirement: English

This is a remote-first role within Canada, meaning international applicants may face limitations unless they already have valid Canadian work authorization. The absence of explicit visa sponsorship suggests this role is best suited for candidates already in the region or with independent eligibility.

💰 Salary Intelligence

💰 Official Salary: Not disclosed
📊 Estimated Range: $110,000 – $160,000 CAD
📈 Level: Mid-Level to Senior

For a healthtech AI startup with Series A funding, this estimated range is competitive within the Canadian market. Candidates with strong LLM deployment and MLOps experience may negotiate toward the upper band, especially given the company’s growth trajectory and technical expectations.

📊 Role Breakdown

This role is heavily execution-focused, with approximately 40% dedicated to model development using frameworks like PyTorch and modern architectures including Transformers and LLMs. Around 25% involves experimentation and optimization, where you will run structured tests using tools like MLflow, Weights & Biases, and hyperparameter tuning frameworks such as Optuna or Ray Tune. Another 20% is allocated to deployment and MLOps, including building CI/CD pipelines, containerization with Docker, and orchestration using Kubernetes.

The remaining 15% focuses on data lifecycle management, including dataset creation, augmentation, and curation. You will also implement model optimization techniques like quantization, pruning, and distillation to improve inference efficiency. This is a high-ownership role requiring continuous iteration, rapid prototyping, and real-world impact delivery in clinical workflows.

🧩 Required Skills & Fit

✅ Must: Strong proficiency in Python and PyTorch
✅ Must: Experience deploying ML models in production environments
✅ Must: Hands-on expertise with LLMs, RAG, and prompt engineering
➕ Bonus: Familiarity with Transformers and MoE architectures
➕ Bonus: Experience with MLOps tools like Kubernetes, MLflow, Kubeflow

📈 Difficulty & Competitiveness

⚡ Level: High
📊 Experience barrier: 3+ years
🧠 Skill complexity: Advanced (LLMs, optimization, MLOps)
🌍 Competition: Global AI talent pool

This is a high-difficulty role due to its demand for both research-level understanding and production engineering skills. Candidates must demonstrate depth across multiple domains, including model optimization, deployment pipelines, and LLM systems. The 3+ years requirement is a baseline—top candidates will exceed it with proven impact.

🚀 Career Impact

⭐ Impact Rating: ⭐⭐⭐⭐☆
🏢 Brand value: High-growth healthtech startup
📚 Skill growth: Advanced AI systems + MLOps
🚀 Future opportunities: Senior ML, AI Architect roles

Joining ExaCare AI positions you at the intersection of AI innovation and healthcare impact. You will gain hands-on experience with cutting-edge ML systems, making you highly competitive for senior-level AI roles or technical leadership positions in the future. The exposure to real-world clinical workflows significantly strengthens your portfolio.

📋 Key Responsibilities

You will design and implement machine learning models using PyTorch and modern architectures, while leading experimentation workflows with tools like MLflow and Weights & Biases. A key part of your role is to deploy scalable models via CI/CD pipelines and optimize them using techniques such as quantization, pruning, and distillation. You will also build and manage datasets, ensuring data quality and robustness. Additionally, you will monitor model performance, detect drift, and maintain reliability in production systems, while continuously iterating on new architectures, especially in the LLM space.

🎯 Application Strategy

🎯 Best apply method: Direct company application
🔥 Highlight: End-to-end ML deployment experience
🔥 Highlight: LLM projects (RAG, fine-tuning, inference optimization)
❌ Avoid: Generic ML resumes without production examples
❌ Avoid: Lack of measurable impact or experimentation rigor

🧠 Application Optimization (Adaptive)

This section is personalized by seniority.

How to use: Paste into ChatGPT, Claude, or Gemini

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Role:
Machine Learning Engineer building production AI systems with LLMs, MLOps, and model optimization.

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🧠 Fit & Positioning Analysis

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📅 Application Signals

This role shows high urgency given recent funding and rapid company growth. Expect strong competition from experienced ML engineers, particularly those with LLM deployment expertise. Early application significantly increases your chances, especially if you can demonstrate real-world impact and production-level ML systems.

🚀 Resume Optimization for This Role

⚡ Takes less than 2 minutes — optimize specifically for this position before applying

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Tailoring your CV to this exact role significantly increases your chances.

Show LLM Experience: Detail RAG, fine-tuning, prompt engineering production projects

Highlight MLOps Pipelines: Demonstrate CI/CD, Docker, Kubernetes model deployment workflows

Quantify Model Impact: Include latency reduction, accuracy gains, cost optimization percentages

Emphasize Experimentation: Showcase MLflow or Weights & Biases tracking workflows

Include Optimization Techniques: List quantization, pruning, distillation applied in production

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✅ Job Source & Verification

This job listing is based on an official company posting and reflects verified role details from ExaCare AI. The information has been structured for clarity and accuracy, ensuring source credibility and alignment with real hiring requirements. Last updated: April 2026.