Machine Learning Engineer, Search

As a Machine Learning Engineer at Realtor.com®, you will be responsible for designing, building, and optimizing machine learning models that drive our core search ranking and relevance systems. You'll also work on personalizing user experiences through recommendation algorithms and assist in the development of ETL pipelines for processing large datasets, ensuring real-time search intelligence.

To qualify for the Machine Learning Engineer role at Realtor.com®, you should have a BS/MS in computer science, statistics, mathematics, or a related field. Foundational knowledge of machine learning concepts, proficiency in Python and SQL, and familiarity with ML frameworks like TensorFlow or PyTorch are highly sought after. An eagerness to learn new technologies is essential.

In the Machine Learning Engineer role at Realtor.com®, proficiency in Python and SQL is crucial. Knowledge of machine learning frameworks such as TensorFlow or PyTorch will give you an additional edge in developing and deploying machine learning models.

At Realtor.com®, collaboration is an integral part of the culture for a Machine Learning Engineer. You will work closely with engineering, product, and data science teams to implement search-driven experiences and ML solutions. The focus on in-person collaboration strengthens teamwork and fosters a creative work environment.

Absolutely! Realtor.com® emphasizes personal and professional growth. You'll have opportunities for hands-on experience with large-scale data and ML systems, and our tuition reimbursement program supports further education. The collaborative culture allows you to learn from experts and innovate in your field.

When answering, highlight specific projects where you designed, built, or optimized machine learning models. Discuss the techniques you used, like supervised or unsupervised learning, and any frameworks you're familiar with like TensorFlow or PyTorch.

Discuss your methodology for analyzing user behavior data and your experience with A/B testing. Explain how you would integrate feedback mechanisms to refine search algorithms based on performance metrics.

Mention specific instances where you utilized Python for data manipulation and model building. Additionally, describe how you've used SQL for data retrieval and processing within your projects.

Provide an example of a project where data analysis led to actionable insights. Focus on the data collection process, the analytical methods applied, and the impact on the project outcome.

List out the machine learning frameworks you've used, like TensorFlow or PyTorch. If possible, explain a project where you effectively applied these frameworks and the specific contributions you made.

Discuss the various steps involved in data preprocessing, like cleaning, normalization, and feature selection. Illustrate how these steps ensure the quality of data fed into your models.

Share resources you utilize such as academic journals, webinars, online courses, or the AI community forums. Emphasize your commitment to continuous learning in the evolving field of AI.

Discuss strategies such as hyperparameter tuning, model selection, and cross-validation. Provide an example from your experience that demonstrates successful optimization efforts.

Explain a structured approach where you would analyze the model's performance, revisit data quality, review feature selection, and possibly employ different algorithms or techniques to enhance performance.

Highlight the significance of team dynamics, communication, and collaborative problem-solving in your role. Share past experiences where teamwork led to successful project outcomes.