Senior Machine Learning Engineer

The Senior Machine Learning Engineer at Attentive plays a crucial role in developing machine learning models and infrastructure that enhance our AI-powered mobile marketing platform. This position involves working with cross-functional teams to build scalable, data-driven products that personalize consumer engagement. You'll be responsible for ensuring the quality and efficiency of systems while embracing innovative solutions to drive results.

To be considered for the Senior Machine Learning Engineer position at Attentive, you should have a minimum of six years of professional experience in machine learning or data analysis. Proficiency with tools like TensorFlow, PyTorch, and a strong command of Python are essential. Your experience working collaboratively on scalable systems and navigating the challenges of developing data-driven products will also be highly valued.

At Attentive, we pride ourselves on fostering a culture of growth and innovation. As a Senior Machine Learning Engineer, you'll have access to continuous learning opportunities and the chance to work on diverse projects that challenge your skills. Our collaborative environment encourages experimentation and feedback, ensuring that you can develop professionally while making a significant impact on our AI-powered platform.

As a Senior Machine Learning Engineer at Attentive, you'll work with a variety of cutting-edge technologies, including TensorFlow and PyTorch for building models, AWS for hosting infrastructure, and Kubernetes for managing deployment. Additionally, you’ll utilize tools like SQL, Spark, and other Python libraries like Pandas and Matplotlib for data analysis and visualization, helping to drive the effectiveness of our marketing solutions.

The culture at Attentive for Machine Learning Engineers is vibrant and collaborative, centered around innovation and customer success. We value team members who are proactive, passionate about their work, and eager to contribute to a shared vision of transforming brand engagement. Our team celebrates diverse backgrounds and perspectives, promoting a healthy balance of ideas and creativity in problem-solving.

Attentive prioritizes high-quality standards in machine learning projects through rigorous testing and validation techniques. As a Senior Machine Learning Engineer, you’ll be responsible for maintaining these standards and proactively implementing quality control measures. Our commitment to product innovation means continually evolving our processes to prevent regressions and optimize model performance across the platform.

The Senior Machine Learning Engineer at Attentive has a significant impact by driving the development of scalable systems that enhance personalized marketing strategies for our customers. Your work will directly influence how brands engage with their audiences, ultimately contributing to the overall success and growth of Attentive as a leader in AI-driven mobile marketing.

When discussing a challenging machine learning project, focus on the specific problems you faced, the methodologies you employed, and any adjustments you made along the way. Highlight your problem-solving skills and how you collaborated with others to navigate these challenges successfully.

Begin by explaining the various testing techniques you use, such as cross-validation, and emphasize the importance of using a validation dataset that reflects real-world scenarios. Discuss how you monitor the model’s performance over time and what steps you take to ensure ongoing accuracy and effectiveness.

When addressing imbalanced datasets, discuss strategies like resampling techniques, synthetic data generation (e.g., SMOTE), and adjusting weightings in the loss function. It's important to elaborate on how these methods improve model performance and provide real-world applications of their effectiveness.

Share specific examples where you successfully worked with product managers, data engineers, or marketing teams. Highlight your communication skills, how you ensured everyone was aligned, and what the collaborative process taught you about cross-functional synergy.

Discuss various resources you engage with, such as industry journals, online courses, community meetups, and conferences. Emphasize your commitment to continuous learning and how you integrate new knowledge into your work.

Explain strategies like regularization, pruning decision trees, early stopping, and cross-validation techniques to manage overfitting. Provide insights into your thought process when selecting the right model complexity based on the dataset.

Mention libraries and frameworks such as TensorFlow, PyTorch, and Scikit-learn. Highlight your experience with them and why you prefer specific tools for certain tasks, showcasing your expertise.

Outline the steps for deploying a model, including data pre-processing, monitoring performance, setting up retraining pipelines, and ensuring scalability. Discuss collaboration with software engineering teams for seamless integration.

Discuss metrics like accuracy, precision, recall, F1 score, ROC-AUC, or custom metrics based on the business needs. Explain how these metrics guide your improvement strategies and model selection.

Share insights into your feature engineering process, discussing how you identify, create, and select relevant features that improve model performance. Providing examples where appropriate will enhance the discussion and showcase your analytical skills.