Experienced Machine Learning Engineer

As an Experienced Machine Learning Engineer at Plaid, you'll be responsible for designing and deploying scalable machine learning solutions, experimenting with advanced modeling techniques, and collaborating cross-functionally to define the ML roadmap. You'll also engage in the full lifecycle of model development, from offline training to online serving and monitoring, ensuring that your impact empowers millions of users through our financial tech products.

To apply for the Experienced Machine Learning Engineer position at Plaid, candidates should have at least 5 years of experience in training and serving AI/ML models in production environments, along with the ability to code independently using tools such as Python and Spark. A Master’s degree in Computer Science, Mathematics, Engineering, or a related field is preferred, but we also consider equivalent work experience.

Plaid’s mission to unlock financial freedom drives the work of the Experienced Machine Learning Engineer. By developing advanced machine learning models and systems, you will play a critical role in improving user interactions with their financial lives. Your innovations will support developers and enhance applications that help users access necessary financial services seamlessly.

Successful candidates for the Experienced Machine Learning Engineer position at Plaid should be proficient in ML modeling techniques including NLP, anomaly detection, and time series forecasting. Experience building data-intensive applications, knowledge of infrastructure tools such as Spark, Jupyter notebooks, and standard ML libraries will also be crucial for success in this role.

Absolutely! Plaid is deeply committed to building a diverse and inclusive team. We believe that varied backgrounds and perspectives enhance our innovation and effectiveness as a company. We encourage applicants from all walks of life to apply for the Experienced Machine Learning Engineer role, as we value what unique experiences and insights you can bring.

When answering this question, focus on the project's objectives, the methodologies you employed, and the outcomes. Highlight how your contributions led to measurable improvements, such as increased efficiency or user engagement. Be specific about your role and what you learned through the process.

Discuss specific challenges you’ve faced, such as data quality issues or integration difficulties. Explain the steps you took to address these challenges and why they were effective. Show your problem-solving skills and highlight any collaborative efforts with your team.

When answering this question, emphasize the importance of aligning model development with business goals and user needs. Discuss how you assess potential impact, feasibility, and resource availability when prioritizing projects.

In your response, briefly explain that supervised learning uses labeled datasets to train models to make predictions, while unsupervised learning deals with unlabeled data, focusing on finding hidden patterns or intrinsic structures. Provide examples of each type to illustrate your points.

Discuss common pitfalls such as overfitting, data leakage, or biases in training data. Explain strategies to avoid these issues, such as employing cross-validation, ensuring data integrity, and being mindful of diversity in training datasets.

Outline the metrics and methods you use to evaluate model performance, such as accuracy, precision, recall, or AUC-ROC curves. Discuss the importance of using benchmarks and validation sets to ensure models generalize well to unseen data.

Explain the integral role of data preprocessing in cleaning, transforming, and preparing your data for modeling. Discuss techniques such as normalization, encoding categorical variables, and handling missing values, highlighting their impact on model performance.

Mention specific resources you utilize to stay informed, such as academic journals, conferences, online courses, webinars, and professional networks. Stress the importance of continuous learning in the rapidly evolving field of machine learning.

When giving your answer, describe the scenario clearly, emphasizing your ability to communicate effectively. Focus on how you simplified the concepts and ensured understanding while framing it in the context of the audience’s interests or concerns.

In your answer, convey your passion for machine learning and how it connects with Plaid's mission to empower financial freedom. Discuss how the role aligns with your career goals and values, mentioning any specific technologies or projects that excite you about Plaid.