Senior Machine Learning Engineer

As a Senior Machine Learning Engineer at Affirm, you will be responsible for developing and optimizing machine learning models that predict loan defaults and facilitate approval decisions. Your roles will include collaborating with engineering teams to create training and monitoring systems, implementing data pipelines that are vital to production models, and researching innovative solutions for credit decisioning.

To apply for the Senior Machine Learning Engineer position at Affirm, you need at least 6 years of experience in machine learning engineering, or an equivalent PhD experience counting for up to 2 years. A Bachelor’s degree in a relevant field is also required, along with strong skills in Python and familiarity with large-scale distributed systems.

Affirm fosters an environment that emphasizes growth and collaboration for Senior Machine Learning Engineers. You will work with a talented team to enhance your expertise and be encouraged to pursue innovative projects that not only benefit Affirm but also advance your professional journey in machine learning.

Essential programming skills for a Senior Machine Learning Engineer at Affirm include proficiency in Python, along with experience in using data manipulation tools like SQL. Familiarity with machine learning libraries such as scikit-learn, NumPy, and deep learning frameworks like PyTorch will also be highly beneficial.

Yes, the Senior Machine Learning Engineer position at Affirm is remote-friendly! Affirm embraces a remote-first culture, allowing you to work from anywhere within Canada while enjoying the flexibility and benefits that come with it.

When answering this question, focus on specific projects where you successfully built and deployed machine learning models. Highlight the problem you solved, the tools and technologies you used such as Python and TensorFlow, and any measurable impact your model had on business outcomes.

Demonstrate your understanding of data preprocessing techniques and feature selection methods. Discuss your approach to cleaning data, handling missing values, and using methods like recursive feature elimination or Lasso for feature selection, and explain how these processes improve model performance.

Discuss any experience you have with tools and technologies such as Spark or Ray that facilitate scalable machine learning. Highlight how you've tackled challenges related to deploying and managing large-scale applications and the importance of choosing the right architecture.

Explain the metrics you use to assess models, such as accuracy, precision, recall, and F1 score. Emphasize your ability to use validation techniques like cross-validation to ensure robust model performance.

This question tests your communication skills. Choose a concept like generalization or overfitting, and explain it in everyday language. This will showcase your ability to communicate complex ideas clearly, which is essential for cross-functional collaboration.

Elaborate on your collaborative experiences, emphasizing how effective communication and teamwork led to successful projects. Mention scenarios where you worked with product or engineering teams to ensure alignment and achieve a common goal.

Show your commitment to continuous learning by mentioning relevant blogs, courses, or conferences you follow. Discuss your strategies for applying new knowledge to your projects, ensuring you remain at the forefront of machine learning advancements.

Describe your familiarity with deployment tools and frameworks like Kubernetes or Docker. Talk about the importance of monitoring models in production and how you have set up monitoring processes to track performance over time and address issues proactively.

Talk about your project management strategies for juggling multiple responsibilities, such as using Agile methodologies, prioritizing tasks based on business impact, and employing tools for organization. Mention how you communicate progress with stakeholders.

Share your passion for machine learning by discussing innovative areas like reinforcement learning or ethical AI. Express how you see these advancements transforming industries and why you want to be part of this evolution, particularly at Affirm.