Machine Learning Engineer III - job 1 of 2

As a Machine Learning Engineer III at Chewy, you will take on significant responsibilities, including developing machine learning models to enhance product offerings and improving the overall shopping experience for pet parents. You will also lead projects from the ground up, collaborating closely with product and engineering teams to refine solutions for Chewy’s onsite and offsite advertising strategies.

To be considered for the Machine Learning Engineer III position at Chewy, you should possess an advanced degree in Operations Research, Statistics, Applied Mathematics, or a related field, along with at least three years of hands-on experience in machine learning. Familiarity with distributed pipelines, optimization, and techniques related to predictive modeling and classification is crucial.

The work environment for a Machine Learning Engineer III at Chewy is collaborative and dynamic. You will be part of a team that values innovation and open communication, working together to develop state-of-the-art advertising solutions that enhance the customer journey and drive business impact.

Chewy actively supports the professional development of its Machine Learning Engineers through opportunities for publishing research papers, establishing model performance standards, and mentoring junior team members. This empowers you not only to contribute to innovative projects but also to grow your skillset within a supportive and inclusive environment.

The expected salary range for a Machine Learning Engineer III at Chewy ranges from $146,500 to $234,500 USD. Factors such as your relevant experience, education, and work location will influence your specific salary, making it a competitive offer tailored to your qualifications.

When preparing to answer this question, share specific projects where you’ve applied machine learning in the advertising realm. Focus on how your models improved click-through rates or enhanced targeting strategies, and ensure you highlight your problem-solving approach and the impact of your work.

In your response, outline your structured approach to model development, starting from identifying the problem, collecting and preprocessing data, selecting the appropriate algorithms, and validating the model. It's important to emphasize the iterative nature of the process and the importance of stakeholder collaboration throughout.

Answer by discussing various evaluation metrics, such as precision, recall, and F1 score, suitable for different types of models. Provide examples of how you have applied these metrics in past projects to ensure accountability and transparency when assessing model performance.

Explain your experience with designing scalable solutions by highlighting your familiarity with distributed systems and cloud services. Discuss specific tools or frameworks you've used, illustrating how they helped to manage large datasets and maintain performance.

Choose a real-life challenge you faced and elaborate on the problem, your thought process, the model you developed, and the outcome. Make sure to emphasize your analytical skills and your ability to navigate complexity effectively.

Share your methods for staying informed, such as attending conferences, participating in forums, or following key researchers in the field. Mention any relevant resources you frequently consult to ensure you're abreast of current trends and technologies.

If applicable, discuss your research contributions, detailing the focus of your papers, the journals or conferences where they were published, and how the findings contributed to the field. If you haven’t published, convey your interest and any plans to do so in the future.

Illustrate your ability to simplify complex concepts by providing examples of past projects where you turned raw data into straightforward recommendations. Emphasize the importance of clear communication tailored for audiences with varying levels of technical expertise.

Explain the importance of teamwork in driving machine learning projects to success. Offer examples of how you've effectively collaborated with cross-functional teams, including product and engineering, to align on objectives and achieve results.

Discuss the significance of ethical practices in machine learning, emphasizing fairness, transparency, and accountability. Share any frameworks or guidelines you adhere to and any experiences you've had addressing ethical challenges in your previous roles.