Machine Learning Engineer

To join PAR Technology Corporation as a Machine Learning Engineer, you’ll need a Master's degree in Computer Science, Software Engineering, or Electrical Engineering. Additionally, a minimum of 18 months of experience as a Computer & Information Research Scientist is required, alongside expertise in technologies such as Python, SQL, AWS, and machine learning frameworks like Scikit-Learn and NLTK.

As a Machine Learning Engineer at PAR Technology Corporation, your primary responsibilities will include collaborating with cross-functional teams to develop machine-learning technologies, owning the ML development lifecycle, building predictive engines for customer intelligence, and advocating for a data-driven culture within the company.

A Machine Learning Engineer at PAR Technology Corporation should be well-versed in programming languages such as Python and SQL. Additionally, familiarity with technologies like Apache Spark, Airflow, and libraries such as NumPy and Pandas will be crucial for developing machine learning models.

Yes, PAR Technology Corporation offers the flexibility to work from a home office anywhere in the U.S. This means you can enjoy the benefits of remote work while being an integral part of our innovative team as a Machine Learning Engineer at Punchh.

PAR Technology Corporation promotes continuous learning and development for its Machine Learning Engineers by fostering a culture of collaboration and experimentation. You'll have opportunities to work with cross-functional teams, participate in industry events, and engage in ongoing training to enhance your skills and expertise.

When answering this question, highlight specific algorithms you've worked with such as supervised learning models, clustering techniques, or natural language processing methods. Be ready to explain situations where you utilized these algorithms effectively in projects.

Respond by outlining the stages of the machine learning lifecycle, which typically include problem definition, data collection, data preparation, model training, evaluation, deployment, and monitoring. Share examples from your experience that demonstrate your understanding of each stage.

Discuss methods such as imputation, removing missing values, or using algorithms that support missing data. Share specific instances from past projects where you've successfully addressed the challenge of missing data to improve model accuracy.

Here, you could mention your experience with AWS, emphasizing services you've used, like AWS EMR or Amazon S3. Explain how you've leveraged cloud computing for data storage, processing, or deploying various machine learning models.

Talk about strategies such as cross-validation, hyperparameter tuning, and thorough exploratory data analysis. Be sure to include a clear example of how you’ve implemented these strategies in a past project.

Explain your methodology by demonstrating your critical thinking and analytical skills. Discuss going back to the data cleaning phase, tweaking your algorithm choice, or collaborating with others for fresh insights.

Illustrate how collaboration with cross-functional teams is essential for gathering insights and aligning objectives. Share past experiences where teamwork improved project outcomes and drove innovation.

Mention sources such as academic journals, conferences, online courses, and tech blogs that you follow. Illustrate your commitment to ongoing learning and how it translates into your work.

Discuss how A/B testing is a crucial method for evaluating models and making data-driven decisions. Provide examples of experiments you’ve run to improve model performance.

Share your proficiency with data visualization tools, emphasizing how you’ve used them to interpret results and present findings effectively. Provide examples showcasing your ability to create impactful visualizations.