Senior Machine Learning Engineer - job 1 of 2

As a Senior Machine Learning Engineer at Fetch, your main responsibilities will encompass the end-to-end life-cycle of machine learning processes. This includes data labeling, model development, and deploying these models into large-scale production environments. You will closely collaborate with backend, devops, and data engineering teams to create impactful and efficient solutions, ultimately enhancing our America's Rewards App experience for users.

To qualify as a Senior Machine Learning Engineer at Fetch, you'll need excellent programming skills, particularly in Python, and experience in training ML models using frameworks like PyTorch or TensorFlow. Additionally, familiarity with deploying models in high-traffic production environments and cloud platforms such as AWS is essential. Strong mathematical skills and independent problem-solving capabilities are highly valued in this role.

At Fetch, a Senior Machine Learning Engineer will work with various tools and technologies, including model serving platforms like TFServing, TorchServe, and Triton. Experience with ONNX, TensorRT, and cloud computing environments such as AWS will be beneficial. A good grasp of statistical methods, particularly in A/B testing and causal inference, can also enhance the effectiveness of your ML applications.

Fetch is committed to the ongoing development of its employees. As a Senior Machine Learning Engineer, you can access educational reimbursement opportunities up to $10,000 annually, allowing you to continue learning and stay updated in your field. Furthermore, the inclusive culture at Fetch fosters innovation and encourages you to take on challenging projects that can propel your career forward.

At Fetch, a Senior Machine Learning Engineer can expect a flexible and collaborative work environment, with options to work remotely from anywhere in the United States or in one of our beautiful offices located in Madison, Birmingham, or Chicago. We prioritize a people-first culture that promotes trust, accountability, and innovative thinking while ensuring you have the tools and support needed to succeed.

When answering this question, describe the specific projects where you successfully deployed ML models. Explain the tools and frameworks you used, any challenges you faced, and how you overcame them. Highlight your understanding of scaling and performance tuning, which are critical for high-traffic environments.

In your response, detail your process for identifying, selecting, and manipulating features to improve model performance. Provide examples or techniques you've used, such as interaction features or transformations, to support your strategies in a convincing manner.

Discuss your methods for monitoring model performance over time and the steps you take to retrain or adjust the model as necessary. Emphasizing your proactive approach and the tools you utilize for tracking model performance can demonstrate your depth of knowledge.

Share specific examples of techniques you’ve implemented to enhance model accuracy, such as hyperparameter tuning, using ensemble methods, or experimenting with different algorithms. Demonstrate your analytical skills and a results-oriented mindset.

Explain your strategy for task management, highlighting your organizational skills and ability to prioritize based on project timelines and business impacts. Mention any tools you utilize for project management to showcase your efficiency.

Share a precise instance where you tackled a challenging ML problem. Outline the problem, your approach to solution finding, and the results achieved. Focus on how your work made a tangible difference and demonstrate your problem-solving skills.

Discuss your proficiency with ML frameworks, such as TensorFlow and PyTorch. Provide examples of projects where you’ve utilized these frameworks to demonstrate your hands-on experience and capabilities.

Talk about your practices around code documentation, testing, and version control. Emphasizing the importance of maintainability in your work processes reflects your professionalism and commitment to quality.

Highlight your understanding of model interpretability techniques and tools, like SHAP or LIME, and how you have applied them in real-world scenarios. Offering examples gives insight into your comprehensive understanding of ML.

Share your enthusiasm for the field and any emerging trends or technologies that you believe will shape its future. Discuss specific areas you’re interested in exploring further to convey your passion and proactive learning attitude.