Software Engineer AI/ ML - PEY

The Software Engineer AI/ML position at Seismic involves developing and optimizing models to enhance AI capabilities and improve user experience. You'll work closely with a team of machine learning and software experts, taking your models from conception to deployment in an agile, cloud-based enterprise SaaS environment.

To apply for the Software Engineer AI/ML position at Seismic, candidates should be graduating from a BA/BS/MS program in Machine Learning, AI, Data Science, or Computer Science. Additionally, strong interpersonal skills, a desire to learn and teach, and familiarity with a variety of technologies in AI and data science are essential.

As a Software Engineer AI/ML at Seismic, you will have the opportunity to work with cutting-edge technologies including React, Python, Kubernetes, Azure, AWS, Google Cloud, and various AI/ML frameworks. You'll gain exposure to a wide range of tools and services that are crucial in today's tech-driven environment.

To prepare for the Software Engineer AI/ML co-op internship at Seismic, it's beneficial to strengthen your coding skills in languages like Python and C#, familiarize yourself with the core concepts of machine learning and AI, and gain experience with cloud services like Azure or AWS. Additionally, working on collaborative projects and improving your communication skills will greatly help you succeed.

At Seismic, the culture is collaborative, inclusive, and focused on knowledge sharing. You're encouraged to be innovative and contribute your ideas while learning from experienced professionals. The environment supports personal and professional development, making it a great place for interns to grow.

When answering this question, share a specific project where you utilized machine learning. Detail the problem you were solving, the model you chose, and how you evaluated its performance. Highlight your thought process and any challenges you overcame during the implementation.

Discuss projects where you've used Python or C# for AI/ML. Be specific about the libraries or frameworks you employed (like TensorFlow or Scikit-learn for Python) and how you applied them to solve problems or create models.

Mention specific resources you use to keep abreast of developments in AI/ML, such as relevant blogs, online courses, conferences, or journals. This shows your commitment to staying informed and continuously growing in this fast-evolving field.

Provide a structured approach to problem-solving. Talk about breaking the problem down into smaller parts, researching potential solutions, and brainstorming with your team. Highlight your ability to think critically and creatively.

Describe Agile methodologies and highlight your experience working in an Agile environment. Discuss how these practices facilitate flexibility, collaboration, and continuous improvement in software development.

Supervised learning involves training a model on labeled data, while unsupervised learning deals with finding patterns in data without specific labels. Provide examples of algorithms used in both contexts to demonstrate your understanding.

Choose a specific example highlighting your role in the team dynamics, the goal you worked towards, and how you contributed to the outcome. This is a great opportunity to showcase your interpersonal skills and collaboration.

List the tools you’ve used for data analysis, such as Python libraries (Pandas, NumPy) or SQL. Explain how you’ve applied these tools in real projects and the insights you derived from your data.

Talk about the responsibilities of a Software Engineer in the AI/ML development space, including designing algorithms, collaborating with data scientists, implementing models, and optimizing performance. Emphasize your understanding of the entire product lifecycle.

Discuss your approach to task management, such as using tools like Trello or Asana, or methodologies like Kanban. Emphasize the importance of communication and adaptability in ensuring project timelines are met.