Senior Machine Learning Engineer

As a Senior Machine Learning Engineer at Taboola, you will manage the entire algorithmic lifecycle. This includes conducting data analytics, prototyping new ideas, developing algorithmic models for production, and maintaining their performance over time. Importantly, you will convert prototypes into shippable products, driving significant revenue impact, while tackling projects in Internet Personalization, Content Feed, Video Recommendations, and more, utilizing a diverse tech stack to deliver cutting-edge solutions.

To be considered for the role of Senior Machine Learning Engineer at Taboola, candidates should possess an M.Sc. or PhD in Computer Science, Mathematics, Engineering, or a related field. Strong proficiency in Python is essential, along with experience in data analysis and statistical modeling. A minimum of 5 years of hands-on experience with machine learning solutions and a solid foundation in SQL are also key requirements, with preferred skills including deep learning development experiences.

As a Senior Machine Learning Engineer at Taboola, you'll engage with a cutting-edge tech stack that includes Python, Java, TensorFlow, Spark, Cassandra, and Google Cloud Platform, among others. Familiarity with tools like Kafka, Elasticsearch, and AirFlow will also be beneficial as you work to integrate and implement machine learning models in a cloud-based infrastructure.

At Taboola, a Senior Machine Learning Engineer will work on exciting projects that directly impact users and revenue. These include initiatives such as Internet Personalization, developing innovative Content Feeds, Real-Time Bidding systems, and Video Recommendations. You will play a crucial role in enhancing user experience and optimizing content discovery across our platform.

The culture at Taboola is vibrant and supportive. Employees often highlight the importance of learning and growth, collaboration among talented individuals, and the ability to contribute meaningfully. The environment promotes transparency, inclusivity, and a sense of belonging, making it an excellent place for a Senior Machine Learning Engineer to thrive and develop professionally.

When describing a complex machine learning project, focus on the problem statement, your role, the technology stack used, and the final outcomes. Highlight your problem-solving skills, how you approached data analysis, and any algorithms you implemented, as well as the impact the project had on your organization or the users.

It's important to articulate not just which languages you're familiar with but also how you've applied them. For example, if you're skilled in Python, discuss how you optimized algorithms with it, along with any libraries like TensorFlow or scikit-learn that you've utilized in projects.

When answering this question, discuss techniques you've implemented, such as cross-validation, regularization methods, or using ensemble learning techniques. Providing a real example where you successfully mitigated overfitting can add depth to your answer.

Share the metrics you've used, such as accuracy, precision, recall, F1-score, or AUC-ROC. Choose metrics relevant to the problem context you've worked in, explaining your decision-making process when selecting these metrics.

Highlight your commitment to continuous learning, mentioning blogs, conferences, online courses, or research papers you follow. Discuss any specific thought leaders or communities within the machine learning field that inspire you.

Your answer should outline the steps you take to understand the problem domain, gather and preprocess data, select the appropriate algorithms, and evaluate models. Emphasize how you collaborate with stakeholders to ensure alignment on objectives.

Talk about your experience with deployment methodologies, including CI/CD pipelines, containerization, or cloud platforms. Providing a concrete example of a successful deployment can showcase your technical capabilities in this area.

Answer by discussing various techniques you employ, such as imputation methods, data augmentation, or simply dropping missing values based on the context of the dataset. It shows your understanding of practical challenges in data handling.

Use a specific story to detail a challenge, such as data quality issues or scaling problems. Focus on your problem-solving abilities and any techniques you utilized to address the issue successfully.

Discuss your aspirations for growth within the field of machine learning, such as mastering new technologies, leading projects, or contributing to research. Highlight your commitment to professional development and how you see your role in an innovative environment like Taboola.