Senior/Staff Machine Learning Engineer - job 2 of 2

As a Senior Machine Learning Engineer at StackAdapt, your primary responsibilities include designing and implementing robust and scalable real-time data pipelines, applying custom ML algorithms, and enhancing our big data ML pipeline architecture. You'll be collaborating with cross-functional teams, including Data Scientists and Engineering, to deliver innovative solutions that optimize ad performance.

To qualify for the Staff Machine Learning Engineer position at StackAdapt, you should have a strong background in algorithm and software design, expertise in implementing machine learning algorithms, and experience in designing scalable distributed systems. A high GPA from a renowned Computer Science program or equivalent industry experience in a competitive tech company is also preferred.

The team culture at StackAdapt is friendly, collaborative, and innovative. As a Machine Learning Engineer, you'll be part of a diverse team that emphasizes supportive communication and teamwork, encouraging all members to share their ideas and contribute to exciting projects in the ever-evolving digital advertising landscape.

In the role of Senior Machine Learning Engineer, you'll engage in a variety of projects that involve developing state-of-the-art ad optimization models, executing real-time data processing, and addressing multifaceted problems with large datasets. This role allows you to leverage your skills while continuously learning and adapting in a fast-paced environment.

Absolutely! StackAdapt values professional growth and supports various personal development initiatives, such as attending conferences, training courses, and webinars. We encourage our Machine Learning Engineers to stay updated on the latest trends in technology and machine learning to ensure continuous improvement.

When discussing your machine learning project, highlight the problem you aimed to solve, the data you used, the algorithms implemented, and the outcome of the project. It's essential to demonstrate your thought process and how you overcame specific challenges throughout the project.

In your answer, mention the specific algorithms you have experience with, such as decision trees, neural networks, or clustering algorithms. Provide examples of where you have successfully applied these algorithms in real projects and discuss the results you achieved.

Talk about your experience with data preprocessing techniques, scalable data architecture, and using distributed computing resources. Provide examples of how you've optimized pipeline performance to ensure your models run efficiently on large-scale datasets.

Discuss the importance of validation techniques, such as cross-validation and holdout testing. Explain how you monitor model performance using metrics like accuracy, precision, and recall, and touch on the process of iterating on your model based on these evaluations.

In your response, share specific platforms you've worked with, such as AWS or Google Cloud, and explain how you've utilized their ML services or managed infrastructure for deploying machine learning models. Highlight any challenges you encountered and how you resolved them.

Here, articulate the challenges you see in machine learning today, such as data quality, bias, overfitting, or model interpretability. Discuss your strategies for addressing these challenges in your projects, showing your critical thinking and proactive approach.

Explain your strategies for writing clean, modular code and using version control effectively. Talk about the importance of documentation and testing in maintaining your models, and share any experiences where you successfully handled scalability challenges.

Discuss various feature selection techniques you've employed, such as recursive feature elimination, LASSO regression, or tree-based methods. Illustrate your decision-making process in selecting relevant features that enhance model performance.

Share your experience collaborating with diverse teams, emphasizing your communication style and collaborative mindset. Provide specific examples of past interactions that led to successful project outcomes, highlighting any challenges you overcame when working with different stakeholders.

Discuss recent advancements you've been following, such as advancements in deep learning, reinforcement learning, or natural language processing. Talk about why these trends interest you, and how you see them impacting the future of machine learning applications in digital advertising.