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Remote Python Sdet Jobs in Ontario (NOW HIRING)

YO AI Labs

AI/ML Software Engineer - Remote

Ottawa, ON · Remote

$80 - $120/hr

Design tasks involving bug fixing, feature development, refactoring, and performance optimization ... Strong proficiency in Python 3, Java, Rust, C++, or TypeScript . * Strong understanding of ...

Celestica International LP

QA Engineer

Toronto, ON · Remote

Req ID: 138664 Remote Position: Hybrid Region: Americas Country: Canada State/Province: Ontario ... Working closely with local development teams in Toronto and collaborating with regional teams, your ...

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Remote Python Sdet information

What is the difference between Remote Python Sdet vs Remote QA Engineer?

AspectRemote Python SdetRemote QA Engineer
Required SkillsPython, automation, testing frameworks, scriptingTesting methodologies, manual and automated testing, scripting
Work EnvironmentDevelopment teams, automation focus, scripting tasksTesting teams, manual and automated testing, test case design
CertificationsISTQB, Python certifications, automation toolsISTQB, software testing certifications
Industry UsageTech, software development, startupsTech, finance, healthcare, various industries

Remote Python Sdet roles focus on developing automated testing solutions using Python, scripting, and testing frameworks, often working closely with developers. Remote QA Engineers handle both manual and automated testing, designing test cases, and ensuring software quality across various industries. While both roles require testing knowledge, the Sdet emphasizes automation and scripting skills, whereas QA Engineers may focus more on manual testing processes.

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Remote Python Sdet jobs near you

AI/ML Software Engineer - Remote

Ottawa, ON • Remote

YO AI Labs
See more jobs at YO AI Labsexternal

$80 - $120/hr

Full-time

Posted 18 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.