Machine Learning Engineer

As a Machine Learning Engineer at Tagup, you'll be responsible for developing and deploying analytic models on industrial data, integrating new data sources into our data pipeline, building and maintaining data processing tools, and ensuring model performance through rigorous validation. You'll collaborate directly with customers to identify valuable data sources to support their industrial processes, contributing to both technical reports and client-facing applications.

To qualify as a Machine Learning Engineer at Tagup, you should have at least 4 years of experience in machine learning, strong programming skills in languages such as Python, and experience managing large cloud infrastructure using services like AWS or Azure. Familiarity with data stores like MySQL or MongoDB is also essential. Bonus experience with frameworks such as PyTorch or those focusing on variational inference will set you apart.

Yes, experience with cloud services is crucial for the Machine Learning Engineer position at Tagup. Successful candidates will have hands-on knowledge of managing and building cloud infrastructure as code, especially with providers like AWS, Azure, or GCP. Mastery of deployment tools such as Terraform and Ansible also greatly enhances your suitability for the role.

As a Machine Learning Engineer at Tagup, you'll work on exciting, real-world projects that involve developing data processing workflows and implementing machine learning models to optimize industrial operations. This includes everything from data ingestion from various sources to creating analytical models and presenting results in web applications that clients interact with.

Tagup fosters a team-oriented and collaborative culture that empowers Machine Learning Engineers to innovate and grow. We emphasize regular outings, an inclusive work environment, and personal ownership within the company. Tagup is committed to attracting and retaining top tech talent, creating a workspace where professionals can thrive and make a tangible impact.

When answering this question, choose a project that showcases your skills and knowledge. Talk about the problem you aimed to solve, the data you worked with, the models you deployed, and the impact your work had on the organization. Provide specific metrics or results to substantiate your success.

Discuss your methods for feature selection, including any tools or techniques you utilize, like correlation matrices or Recursive Feature Elimination (RFE). Explain your thought process and how you consider the relevance of features concerning model performance.

Mention languages you are proficient in that are relevant to the role, such as Python or Java. Provide examples of how you’ve used them in machine learning projects, highlighting any libraries like Scikit-learn or TensorFlow you’ve worked with.

Explain your approach to data quality checks, including preprocessing steps like dealing with null values and outlier detection. Share specific techniques or tools you apply to maintain high data integrity throughout the modeling process.

Choose an experience where you effectively simplified technical concepts for a non-technical audience. Highlight how you tailored your explanation, using analogies or visual aids to convey the importance and workings of the model.

Be specific about your experience with cloud infrastructure management. Discuss the services you've used, such as AWS or Azure, and the deployment tools you've mastered, such as Terraform or Ansible. Provide examples of how you’ve architected solutions in the cloud.

Detail the methods you use to evaluate model performance, such as cross-validation and the use of metrics like accuracy, precision, recall, and F1 score. Explain how you leverage insights from these validations to iterate on your model designs.

Reflect on your experience with various datasets relevant to machine learning applications. Be specific about the characteristics of these datasets and discuss how they influenced your modeling strategies.

Discuss your approach to continuous learning, such as attending conferences, participating in online courses, or following industry blogs. Mention any specific resources or communities that you find particularly helpful for staying informed.

Share your insights and predictions on the role of machine learning in optimizing industrial processes. Discuss emerging trends you believe will shape the future, such as predictive maintenance or autonomous systems, and how they align with the mission of Tagup.