Remote ML Technical Quality Assurance Lead - AI Trainer

SuperAnnotate; Remote; CAD 80,000 - 100,000; Full time

Job summary

A tech-focused company is seeking a Remote ML Technical Quality Assurance Lead in Kelowna, British Columbia. This pivotal role involves ensuring high standards in AI training by reviewing Python and ML tasks, providing feedback, and maintaining documentation. The ideal candidate will possess strong analytical skills, extensive ML experience, and a degree in a relevant field. The position also includes onboarding trainers and communicating actively with the team. Join us and contribute to our innovative AI projects.

Qualifications

• Solid professional experience with Python for machine learning workflows.
• Strong hands-on experience with ML libraries and frameworks.
• Exceptional ability to analyze ML code and propose improvements.

Responsibilities

• Review Python/ML tasks and code submissions for correctness.
• Communicate updates and respond to queries via Discord.
• Monitor trainer/QA activity and encourage re-engagement.
• Create and improve technical documentation.
• Organize onboarding and training calls.

Skills

Python

Machine Learning

NumPy

pandas

scikit-learn

PyTorch

TensorFlow

Analytical skills

Written communication

Education

Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, Statistics

Job description

Remote ML Technical Quality Assurance Lead - AI Trainer SuperAnnotate•Kelowna, British Columbia, CA

re you ready to take on a pivotal role in ensuring the highest standards in AI training? We are looking for a ML Technical Quality Assurance Lead to help ensure consistently high standards in our AI data training projects. This role combines the opportunity to work with modern machine learning systems with the responsibility of maintaining high-quality Python and ML content, making it a strong fit for people who are passionate about both technology and precision.

You will play a crucial role in guaranteeing the technical quality and consistency of ML‑related training data, code, and evaluations, ultimately contributing to the success of our projects.

Key Responsibilities

• Technical quality review: Review Python / ML tasks and code submissions (scripts, notebooks, experiments) for correctness, clarity, model design, and alignment with project guidelines; provide clear and constructive feedback and elevate critical issues when needed.
• Communication: Keep trainers and QAs updated on Discord about new items, ML‑related clarifications, or project changes, and respond to their technical and process questions in a timely and professional manner.
• Activation management: Monitor trainer / QA activity and proactively communicate with inactive contributors to understand blockers and encourage re‑engagement.
• Documentation: Create, maintain, and improve technical documentation such as ML coding guidelines, experiment checklists, best‑practice examples, trackers, FAQs, honeypots, and related documents.
• Onboarding & training: Schedule, organize, and run onboarding and training calls with trainers / QAs to walk them through ML quality standards, common issues, and review expectations.

Your Profile

• Academic / professional background: Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, Statistics, or a related field.
• Python & ML experience: Solid professional experience with Python for machine learning, including end‑to‑end workflows (data preparation, model training, evaluation, and deployment or experimentation).
• ML stack: Strong hands‑on experience with NumPy, pandas, scikit‑learn, and at least one deep learning framework such as PyTorch or TensorFlow.
• Analytical & problem‑solving skills: Exceptional ability to understand and evaluate ML code, experiments, and metrics, quickly identify issues, and propose clear improvements.
• Written communication: Excellent written communication skills in English, able to explain technical feedback, reasoning, and recommendations clearly to a distributed team.
• Managing ambiguity: Comfortable working with incomplete or evolving ML specifications and able to clarify complex technical requirements effectively.
• Leadership mindset: Comfortable working independently, giving feedback, and keeping the trainer / QA community engaged, aligned with standards, and supported.