Machine Learning Engineer

As a Machine Learning Engineer at Dstillery, your responsibilities will include developing MLOps processes and tools, collaborating with fellow engineers and researchers to create innovative machine learning products, building and deploying machine learning infrastructure, writing production code and creating data pipelines, as well as conducting code reviews to ensure best practices are upheld across the organization.

To qualify for the Machine Learning Engineer role at Dstillery, candidates should possess a bachelor’s degree in a quantitative field such as Computer Science, Electrical Engineering, Statistics, Physics, or Mathematics, along with at least 2 years of industry software engineering experience. A passion for clean code, automation, and a willingness to learn various technologies used at Dstillery are also essential.

Machine Learning Engineers at Dstillery primarily work with Python and various machine learning frameworks including scikit-learn, TensorFlow, and PyTorch. Additionally, familiarity with tools and platforms such as Google Cloud Platform, Airflow, Spark, SQL, Kubernetes, JenkinsX, and Terraform is beneficial, although on-the-job training is provided.

Yes, the Machine Learning Engineer position at Dstillery offers the flexibility of remote work or can be based in our New York office. We understand the importance of work-life balance and strive to accommodate various working preferences.

Dstillery fosters a welcoming and inclusive work culture, valuing diverse voices and backgrounds. We believe that this diversity leads to innovative solutions and perspectives. Our work environment is supportive, focused on collaboration, and upholds a strong work-life balance, making it an ideal setting for a Machine Learning Engineer to thrive.

In answering this question, focus on specific projects where you've used cloud platforms like Google Cloud Platform. Discuss your involvement in deploying machine learning models in the cloud, any challenges you faced, and the solutions you implemented to overcome those challenges.

Be prepared to discuss your experience with frameworks such as TensorFlow, PyTorch, or scikit-learn. Provide examples of projects where you've implemented these tools, what problems they solved, and your role throughout the process.

Talk about your testing and validation strategies, such as cross-validation, hyperparameter tuning, and monitoring metrics over time. It's essential to convey your understanding of model evaluation techniques and continuous improvement processes.

Discuss the importance of open communication and regular meetings in a collaborative environment. Provide examples of how you've successfully worked in teams, shared knowledge, and built relationships to drive project success.

Choose a concept such as supervised vs. unsupervised learning, or overfitting vs. underfitting. Break it down in layman's terms and relate it to real-world examples to demonstrate your communication skills and technical understanding.

Share your approach to continual learning through online courses, attending webinars, reading research papers, or participating in relevant forums and communities. This shows your proactive attitude towards professional growth in the field.

Outline a specific project where you faced difficulties—like data collection issues or technical limitations. Explain your approach to troubleshooting and how you implemented solutions or pivoted to ensure project success.

Discuss practices like code reviews, adhering to coding standards, proper documentation, and writing tests. Giving examples of how you maintain a clean codebase will demonstrate your commitment to software quality.

If applicable, describe your experience with MLOps tools and processes you’ve used to automate deployment and monitor machine learning models. Share how these practices improved workflow and efficiency in your projects.

Articulate the importance of the Machine Learning Engineer’s role across the product development lifecycle, from defining requirements and prototyping to deployment and post-launch monitoring, emphasizing collaboration with multiple teams.