Machine Learning Engineer

As a Machine Learning Engineer at Quantcast, your main responsibilities will include designing and developing ML applications, running experiments to test new modeling ideas, and optimizing performance to meet the demands of real-time systems. You’ll need to write clean, maintainable code, collaborate with other engineering teams, and keep pace with the latest machine learning developments.

To qualify for the Machine Learning Engineer position at Quantcast, candidates need 1-3 years of experience, strong fluency in programming languages like Python or Java, and solid skills in mathematics and statistics. Familiarity with various ML algorithms and an interest in software design and engineering are also important.

Quantcast supports the professional development of Machine Learning Engineers by encouraging continuous learning and participation in code reviews. The company promotes sharing knowledge among team members and staying updated on industry advancements, fostering an environment that values skill enhancement.

At Quantcast, the expected base salary range for a Machine Learning Engineer is $151,300 to $204,700, reflecting your individual skills and expertise, along with possible performance bonuses and equity options.

Machine Learning Engineers at Quantcast will work on diverse projects involving real-time ML systems that predict various characteristics of bid requests, using techniques like NLP and clustering. You’ll have the opportunity to contribute to innovative solutions that enhance advertising effectiveness.

Demonstrating your coding proficiency in Python or Java is crucial. Discuss specific ML projects where you implemented algorithms, providing insights into your coding style, problem-solving abilities, and how you applied industry best practices.

Staying updated is key in this field. Talk about your routine for following research papers, participating in online courses, and engaging with professional communities to continuously enhance your knowledge of new algorithms and methodologies.

Highlight your understanding of various performance optimizations like profiling code, reducing memory usage, and using efficient algorithms. Providing examples of past experiences will enhance your answer further.

Share a clear example, outlining the problem, your approach in building the model, the algorithms used, and the tangible impact it had on the outcome of the project. Focus on metrics that demonstrate your model's success.

Discuss your systematic approach to testing ML systems by validating model accuracy, performance benchmarks, and conducting A/B testing. Emphasize the importance of understanding edge cases and data variation in your testing methodology.

Describe specific experiments you’ve carried out, detailing the hypotheses, methodologies used, and the outcomes. Highlight your ability to learn from the results and iterate on your findings.

Focus on challenges like data quality, latency issues, and system bottlenecks. Discuss the strategies you employed to overcome those challenges and the lessons learned during the process.

Talk about practices you follow such as writing unit tests, peer code reviews, and adhering to coding standards, while explaining how these practices contribute to maintaining high code quality in collaborative environments.

Discuss your experience in team settings, particularly in terms of participating in code reviews, sharing feedback, and contributing to collaborative projects. Highlight your communication skills and experience in cross-team collaboration.

Express your passion for machine learning and how its potential to solve real-world problems inspires you. Discuss any specific areas within machine learning that capture your interest and why you’re eager to contribute to that space.