AI Engineer

As an AI Engineer at Capsule, your main responsibilities will include researching and evaluating machine learning techniques to enhance our video editing tools, collaborating with cross-functional teams to identify key ML feature development opportunities, and developing and implementing ML models and algorithms that power our features. You'll also focus on optimizing model performance through testing, participate in code reviews, and keep abreast of the latest trends in ML engineering.

The AI Engineer role at Capsule requires a minimum of 3 years of experience as an AI/ML engineer in a commercial software development setting, along with a degree in Computer Science or a related field. Proficiency in Python and familiarity with deep learning frameworks such as PyTorch is essential. Additionally, skills in data preprocessing, feature engineering, and production deployment of ML models are prioritized.

At Capsule, AI Engineers will be part of a vibrant, zero-ego work environment filled with highly-skilled team players. The emphasis is on collaboration, innovation, and enjoying the rapid pace of an early-stage startup. You'll have the freedom to bring new ideas to the table and work closely with our founders to shape the future of our product.

Capsule fosters a culture of continuous learning and innovation. AI Engineers are encouraged to stay up-to-date with the latest developments in machine learning engineering, participate in code reviews, and share technical guidance. This creates a rich environment for professional growth and the opportunity to contribute effectively to high-impact projects.

Capsule offers competitive compensation that includes salary and stock options, comprehensive insurance benefits covering medical, dental, vision, and life insurance, as well as generous paid time off. We prioritize the well-being of our team members and offer an engaging and rewarding work environment.

In your response, highlight specific projects where you've built and deployed ML models. Detail the techniques you used, data sources, and the impact your work had on the project. Share any particular challenges you faced and how you overcame them.

Discuss the methods you utilize for feature selection, such as correlation measures or algorithm-based techniques. Detail your iterative optimization process, including the metrics you use to gauge model performance and how you fine-tune parameters for success.

Talk specifically about your experience with frameworks like PyTorch or TensorFlow. Share examples of projects where you've utilized these tools, your role in the development, and the outcomes you achieved using the frameworks.

Mention the resources you utilize such as industry publications, online courses, or community forums. Explain how you incorporate new knowledge into your work and perhaps how you have implemented recent trends in past projects.

Select a specific scenario that highlights your problem-solving skills. Discuss the challenge, your thought process in addressing it, the steps you took, and the ultimate resolution. Make sure to focus on what you learned.

Outline your approach to model validation, including techniques like cross-validation or splitting datasets. Discuss how you assess performance with metrics like accuracy, precision, or recall and why these metrics matter for the specific model.

Talk about your communication strategies that help translate complex technical concepts into terms understood by non-technical stakeholders. Share examples of how your collaboration improved project outcomes.

Emphasize the importance of data preprocessing, including cleaning, normalization, and transformation. Discuss any specific tools or methods you've successfully employed and how they contributed to the model's performance.

Express your enthusiasm for innovation and the opportunity to make a significant impact in a fast-paced environment. Mention the collaborative culture and how it aligns with your career aspirations and desire for continuous learning.

Describe a specific project in which you've taken a model from development to production. Share the deployment process, any challenges faced during deployment, and the eventual outcome. Highlight your role and contributions throughout the process.