Machine Learning Engineer - job 2 of 2

As a Machine Learning Engineer at StackAdapt, you'll be responsible for designing modular and scalable real-time data pipelines, implementing custom machine learning algorithms, and developing microservice architectures that run numerous ML models simultaneously. This role is about solving complex problems and enhancing our digital advertising platform's efficiency, ensuring that we effectively manage vast amounts of data.

StackAdapt seeks candidates who hold a high GPA from a respected Computer Science program and have a strong understanding of software design, algorithms, and data structures. Ideal candidates will have experience implementing probabilistic or machine learning algorithms, as well as an interest in developing scalable distributed systems that handle high-end data processing.

At StackAdapt, the work culture is characterized by friendliness and collaboration. As a Machine Learning Engineer, you'll join a diverse team that values inclusivity, creativity, and mutual respect. We prioritize mentorship and support continuous learning, ensuring you have both the environment and resources you need to excel in your role.

StackAdapt is deeply committed to the professional development of its Machine Learning Engineers. We offer access to training and onboarding programs, programmatic courses, and certifications, in addition to opportunities to attend conferences and workshops, fostering a culture of continuous improvement and innovation.

StackAdapt provides an array of benefits for its Machine Learning Engineers, including competitive salary and equity options, RRSP matching, generous vacation policies, and comprehensive health benefits from day one. Additionally, we offer work-from-home reimbursements and a supportive environment for personal development initiatives.

When answering this question, detail a specific project including the problem you aimed to solve, the data you used, the algorithms implemented, and the results achieved. Highlight your contribution and any challenges faced, emphasizing your role in the project.

In your answer, list the algorithms you have experience with, such as regression models, classification algorithms, or clustering techniques. Also, mention contexts in which you applied them to showcase your practical experience.

Discuss your systematic approach to debugging, such as validating data sources, checking for overfitting, and analyzing model performance metrics. Provide examples of specific tools or methodologies you have used in the past.

In your response, detail the steps involved in data preprocessing, such as cleaning, normalization, transformation, and feature selection. Explain how you ensure data quality and robustness before model training.

Mention key performance metrics relevant to the type of model you're working with, such as accuracy, precision, recall, F1 score, and AUC-ROC. Discuss how these metrics guide your model optimization process.

Describe your familiarity with building and maintaining data pipelines. Include information about tools you’ve used (like Apache Airflow or TensorFlow) and how you ensure efficient data flow and processing.

Share specific sources such as academic journals, online courses, conferences or research papers that you follow. Mention that you regularly engage in communities or forums to learn and discuss new trends and technologies in the field.

Highlight your experience with model deployment methodologies, including tools like Docker or cloud platforms like AWS and Azure. Discuss the importance of scalability and maintainability in deployed models.

Emphasize your ability to simplify complex concepts into relatable terms. Use analogies or real-world examples to clarify how machine learning impacts everyday situations, showcasing your communication skills.

Express your passion for machine learning and how it aligns with StackAdapt's vision. Discuss your excitement about working in a collaborative environment focused on innovation and driving impactful results in the digital advertising sector.