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Senior Machine Learning Engineer Jobs in Edmonton, AB

YO AI Labs

AI/ML Software Engineer - Remote

Edmonton, AB · Remote

$80 - $120/hr

Remote Job Summary We are seeking experienced Senior Software Engineers to support an AI training project by creating reinforcement learning environments that evaluate AI models on complex software ...

Tetra Tech

Senior Geotechnical Engineer

Edmonton, AB · Hybrid

Tetra Tech is adding a Senior Geotechnical Engineer to our Arctic Engineering team based in ... Learning and development opportunities for ongoing professional growth. * Mentorship with world ...

Entuitive

Senior Structural Engineer

Edmonton, AB · Hybrid

CA$95K - CA$125K/yr

Entuitive is looking for a Senior Structural Engineer, based in our Edmonton office to be part of ... If you are excited about this opportunity and are interested in learning more about Entuitive ...

Entuitive

Senior Structural Engineer

Edmonton, AB · On-site

CA$95K - CA$125K/yr

Entuitive is looking for a Senior Structural Engineer, based in our Edmonton office to be part of ... If you are excited about this opportunity and are interested in learning more about Entuitive ...

YO AI Labs

Backend Engineer - Remote

Edmonton, AB · Remote

$80 - $120/hr

Remote Job Summary We are seeking experienced Senior Software Engineers to support an AI training project by creating reinforcement learning environments that evaluate AI models on complex software ...

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All JobsMachine Learning Engineer JobsSenior Machine Learning Engineer JobsSenior Machine Learning Engineer Jobs in Edmonton, AB

Senior Machine Learning Engineer information

See Edmonton, AB salary details

$45K

$165.3K

$248.5K

How much do senior machine learning engineer jobs pay per year?

As of Sep 6, 2026, the average yearly pay for senior machine learning engineer in Edmonton, AB is $165,321.00, according to ZipRecruiter salary data. Most workers in this role earn between $137,500.00 and $184,000.00 per year, depending on experience, location, and employer.

What does a senior machine learning engineer do?

A Senior Machine Learning Engineer designs, develops, and implements machine learning models to solve complex problems. They are responsible for selecting appropriate algorithms, preprocessing data, and optimizing model performance. Additionally, they collaborate with data scientists, software engineers, and product teams to integrate machine learning solutions into production systems. Senior engineers also mentor junior team members and contribute to setting technical direction for machine learning projects.

What are some common challenges senior machine learning engineers face when deploying models to production, and how can they be addressed?

Senior Machine Learning Engineers often encounter challenges related to model scalability, maintaining performance in real-world scenarios, and ensuring reliable integration with existing systems. Addressing these challenges typically involves thorough testing, implementing robust monitoring for model drift, and collaborating closely with DevOps and software engineering teams to streamline deployment pipelines. Staying updated on best practices in MLOps and adopting tools for automated deployment and monitoring can greatly improve the reliability and efficiency of production models.

What are the key skills and qualifications needed to thrive as a senior machine learning engineer, and why are they important?

To thrive as a Senior Machine Learning Engineer, you need advanced knowledge of machine learning algorithms, statistical modeling, and programming languages like Python or Java, typically supported by a degree in computer science or a related field. Experience with frameworks and tools such as TensorFlow, PyTorch, scikit-learn, and cloud platforms, as well as familiarity with version control and CI/CD systems, is essential. Strong problem-solving, communication, and leadership skills help you collaborate effectively and mentor junior team members. These capabilities are crucial for designing scalable ML solutions and driving impactful results within complex, dynamic projects.

What is the difference between Senior Machine Learning Engineer vs Data Scientist?

AspectSenior Machine Learning EngineerData Scientist
Required CredentialsBachelor's/Master's in CS, ML, or related; experience with ML frameworksBachelor's/Master's in CS, Statistics, or related; strong analytical skills
Work EnvironmentDevelops and deploys ML models in production systemsAnalyzes data, builds models, and provides insights
Industry UsageTech, finance, healthcare, e-commerceResearch, finance, marketing, tech

While both roles require strong technical skills and knowledge of machine learning, Senior Machine Learning Engineers focus more on deploying scalable ML solutions in production environments, whereas Data Scientists primarily analyze data and develop models for insights. The roles often overlap but differ in their core responsibilities and focus areas.

What job categories do people searching Senior Machine Learning Engineer jobs in Edmonton, AB look for?

The top searched job categories for Senior Machine Learning Engineer jobs in Edmonton, AB are:

What cities near Edmonton, AB are hiring for Senior Machine Learning Engineer jobs?

Cities near Edmonton, AB with the most Senior Machine Learning Engineer job openings:

Senior Machine Learning Engineer jobs near you
Infographic showing various Senior Machine Learning Engineer job openings in Edmonton, AB as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $165,321 per year, or $79.5 per hour.

AI/ML Software Engineer - Remote

YO AI Labs

Edmonton, AB • Remote

$80 - $120/hr

Full-time

Posted 10 days ago

Job description

Senior Software Engineer

Job Type: Contractor (~15 hours/week)
Location: Remote

Job Summary

We are seeking experienced Senior Software Engineers to support an AI training project by creating reinforcement learning environments that evaluate AI models on complex software engineering tasks using Model Context Protocol (MCP) tools.

You will design reproducible environments, deterministic verification, and reference solutions for tasks such as bug fixing, feature implementation, codebase refactoring, and performance optimization. No prior AI experience is required.

Key Responsibilities
  • Create reinforcement learning environments for software engineering tasks.
  • Design tasks involving bug fixing, feature development, refactoring, and performance optimization.
  • Build deterministic verification systems and golden reference solutions.
  • Evaluate AI agents' ability to reason through complex codebases and use MCP tools effectively.
  • Develop realistic, reproducible software engineering scenarios.
  • Ensure tasks accurately measure coding ability, problem-solving, and tool usage.
  • Document solutions and provide clear technical feedback.
Required Skills
  • Strong proficiency in Python 3, Java, Rust, C++, or TypeScript.
  • Strong understanding of algorithms and data structures.
  • Experience with bug fixing and debugging complex software issues.
  • Proven experience in feature implementation and codebase refactoring.
  • Strong knowledge of performance optimization and tuning.
  • Excellent written and verbal communication.
  • Strong attention to detail.
Preferred Qualifications
  • Experience working with large-scale or distributed codebases.
  • Familiarity with AI/ML systems is a plus but not required.
  • Experience with rigorous code reviews and software engineering best practices.
  • Experience working effectively in remote or cross-functional teams.
Hiring Process
  1. Submit an application and screening questions.
  2. Complete an AI interview (~30 minutes).
  3. Complete a technical assessment, if required.
  4. Hiring Manager review.
Compensation

Compensation is output-based, with payment provided per task that meets project specifications. Minimum weekly submission requirements may apply.

Availability

Selected experts should be prepared to begin their first tasks within 24–48 hours of completing onboarding.