As a Machine Learning Engineer (Staff) at GPTZero, you will be primarily responsible for designing, training, and fine-tuning advanced language models. You'll also develop AI agents using retrieval-augmented language models and build scalable ML training and inference systems. Staying updated with the latest research and technologies is vital, as you will address complex and novel problems alongside your passionate team.

To qualify for the Machine Learning Engineer (Staff) position at GPTZero, candidates should have at least 5 years of experience in PyTorch or Transformers and a proven track record of leading impactful machine learning projects. Strong software engineering skills and the ability to create modular codebases are essential. Experience with complex pipelines and a desire to contribute positively to society will set you apart as an ideal candidate.

At GPTZero, you will join a dynamic and experienced team that includes professionals from top organizations. You’ll work closely with seasoned leaders in AI research and engineering who bring their unique insights from previous roles at places like Uber and Microsoft. The collaborative environment fosters innovation and encourages each member to take initiative and lead projects.

The Machine Learning Engineer (Staff) at GPTZero will engage in a variety of projects, focusing on cutting-edge machine learning and AI technology. This includes designing and deploying state-of-the-art language models, developing efficient ML training systems, and creating applications that address societal needs related to quality and transparency of online information. The role is challenging yet rewarding, as your contributions will directly impact millions of users.

GPTZero offers an attractive package of employee perks and benefits for the Machine Learning Engineer (Staff) role. This includes health, dental, and mental health benefits, a hybrid work model in Toronto and NYC, equity opportunities, flexible PTO, and regular company retreats. Plus, you'll have mentorship opportunities with esteemed advisors and a wellness and learning stipend to support your professional growth.

Be prepared to share specific projects where you designed and fine-tuned models, discussing the methodologies and frameworks you used. Highlight any unique challenges you faced and how you overcame them to meet project goals.

Describe your workflow and methodologies you prefer, including how you leverage PyTorch's features or Transformers architecture for model training. Discuss any optimizations or custom techniques you have developed to improve model performance.

Explain your approach to scalability, which may include discussion about the architecture of the models, infrastructure used for deployment, and best practices for creating modular codebases that can adapt as requirements change.

Talk about a project where you played a key role, emphasizing the objectives, results, and how the project improved existing systems or contributed positively to users or stakeholders.

Share how you keep yourself informed on industry trends, research papers, and emerging technologies. Whether through reading journals, attending conferences, or participating in online courses, be sure to demonstrate your commitment to professional development.

Provide a real example that outlines the problem you encountered, the troubleshooting process you followed, and how you ultimately resolved the issue while explaining what you learned from the experience.

Discuss your collaborative approach, emphasizing communication, understanding product vision, and how data science can align with business objectives. Showcase examples where cross-functional teamwork led to successful outcomes.

Detail your experience in developing ML pipelines by outlining the key components and tools you used. Emphasize the importance of automation, testing, and monitoring throughout the pipeline lifecycle to ensure efficiency.

Express your thoughts on the future of machine learning, including ethical implications, model interpretability, data privacy, and combining AI with human-centric approaches. Show your awareness of both the opportunities and potential hurdles ahead.

Convey your passion for GPTZero's mission and how your skills align with the company's goals. Discuss why contributing to a project with a social impact excites you and what you hope to achieve in this role.