Senior Quality Engineer, AI

As a Senior Quality Engineer at Descript, your primary responsibilities include developing detailed test plans, running experiments, creating benchmarks for AI model quality, and ensuring that our products meet the highest standards of reliability. You will also mentor others and collaborate across various teams to refine testing processes.

To apply for the Senior Quality Engineer role at Descript, you should have a Bachelor’s degree in Computer Science or Engineering, 5+ years of QA experience, and at least 2 years leading the evaluation of AI models. Experience collaborating with AI research teams is also beneficial.

Descript fosters an environment of continuous learning and mentorship. As a Senior Quality Engineer, you'll have access to resources for professional development and the opportunity to mentor junior QA team members, allowing you to sharpen your skills while helping others grow.

At Descript, you can expect a collaborative and innovative work culture. We value diversity and believe that different perspectives enhance our products. You'll work alongside passionate colleagues, engage in serendipitous moments of discovery, and contribute significantly to cutting-edge AI technology.

Yes, the Senior Quality Engineer position at Descript offers remote work options. However, we encourage occasional in-person collaboration at our San Francisco headquarters, ensuring you can benefit from shared ideas and teamwork while maintaining flexibility.

The base salary range for a Senior Quality Engineer at Descript is between $140,000 and $181,000, depending on your experience and expertise. We carefully consider various factors to ensure competitive compensation within this range.

As a Senior Quality Engineer at Descript, you'll interact with various technologies including AI models, quality assurance tools, SQL for writing queries, and Python for scripting. Your work may involve both qualitative and quantitative evaluations of cutting-edge AI-powered products.

To create a test plan for an AI product, I would first identify the project requirements and specifications. Then, I'd outline the testing objectives, define the scope, and select appropriate testing methodologies, ensuring to include unit tests, integration tests, and user acceptance testing that are tailored to AI functionalities.

I utilize both quantitative and qualitative methods to identify inconsistencies in AI model behavior, including analyzing model outputs against expected results, running controlled experiments, and repeatedly testing edge cases to ensure coverage and robustness of the AI features.

In a previous role, I identified inefficiencies in the bug tracking system. I introduced a new documentation process that categorized bugs by severity, which enhanced communication between teams and resulted in a 30% faster resolution time for critical issues, significantly improving product reliability.

I prioritize clear, concise, and regular communication. I often set up recurring meetings and use collaborative platforms to share updates on testing progress and issues with cross-functional teams, ensuring everyone stays aligned and informed throughout the development process.

I typically leverage tools like Selenium and JUnit for automated testing, along with custom scripts I develop in Python. These tools allow for efficient, repeatable testing of AI functionalities, helping to streamline the QA process overall.

I've led the evaluation of AI models by focusing on several key metrics, including accuracy, precision, recall, and F1 score. Understanding model performance through these metrics ensures that we deliver high-quality AI products to our users.

I manage tight deadlines by prioritizing tasks using a risk-based approach. This means focusing on high-impact areas first and allocating resources wisely, while ensuring that critical QA processes are not compromised under time pressure.

Feedback is crucial in the testing process. I actively seek inputs during testing phases from peers and stakeholders to identify overlooked areas and enhance testing strategies. Constructive feedback fosters continuous improvement for both individual and team performance.

I have successfully mentored junior QA engineers by establishing a buddy system where they paired with me on testing projects. I provided guidance on best practices and encouraged questions, which resulted in their increased confidence and proficiency in QA methodologies.

I take a proactive approach to learning new technologies by setting aside time for research and hands-on practice. I also engage with online courses and community forums to stay updated, ensuring I can effectively implement new tools and techniques in my QA processes.