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Machine Learning Engineer / Sr. Machine Learning Engineer

SquarePeg

SquarePeg

Software Engineering
Toronto, ON, Canada
Posted 6+ months ago
Who are we?

Our mission focuses on enhancing the quality and transparency of information available online. Our team consists of individuals from top-tier engineering backgrounds and leading research institutions. We are developing innovative models and applying advanced research to create solutions for AI serving millions of active users and major enterprise clients.

What we're looking for:

We believe in creating machine learning models that serve humanity’s best interests.

In this role, you will contribute to developing a next-generation platform aimed at verifying the origin, quality, and accuracy of information globally. The ideal candidate will have a solid foundation in machine learning research, a strong product sense, and exceptional software engineering skills. You will work alongside a dedicated team to build impactful software that has garnered millions of users worldwide.

Responsibilities:

  • Design, train, and optimize advanced language models.
  • Develop AI agents integrated with retrieval-augmented language systems.
  • Create efficient and scalable systems for machine learning training and inference.
  • Stay informed about the latest research and technologies to tackle innovative challenges.
  • Collaborate closely with product and design teams to create user-friendly applications that drive societal change.

Qualifications:

  • 5+ years of experience with PyTorch and Transformers.
  • Proven track record in advancing machine learning technologies.
  • Proactive approach, capable of pitching, planning, and executing projects in a fast-paced environment.
  • Passionate about making a positive impact on society.
  • Willingness to take on various roles and lead as the team expands.
  • Eligible to work in Canada

Bonus:

  • Active contributions to open-source projects.
  • Publications in top-tier machine learning conferences.
  • Experience in early-stage startup environments.
  • Understanding of potential failure modes in machine learning models.