Interview preparation plan: AI platform Engineering Intern at Lockheed Martin

Practice questions (20)

1. Tell me about a time you led a team through a crisis.
2. Describe a situation where you convinced someone of an idea.
3. Tell me about a conflict with a colleague/manager and how you resolved it.
4. Tell me about a project where you collaborated with other teams.
5. Describe a difficult decision you had to make quickly.
6. Tell me about when you had to adapt to a major change.
7. Tell me about a mistake you made and what you learned.
8. Share an example of when you had to motivate your peers.
9. Describe a time you took initiative in a team setting.
10. Tell me about a time you had to give constructive feedback.
11. Describe a situation where you had to manage multiple priorities.
12. Tell me about a time you had to learn something new quickly.
13. Share an experience when you had to work with a difficult team member.
14. Describe a time you had to adapt your communication style.
15. Tell me about a time you had to support a team member.
16. Describe an instance when you had to uphold ethical standards in a group.
17. Tell me about a time when you had to prioritize your own tasks over the team's.
18. Share an experience where you had to compromise.
19. Explain the difference between supervised and unsupervised learning.
20. How would you optimize a machine learning model?

Questions to ask the interviewer (8)

• What does success look like for an AI platform engineering intern at Lockheed Martin?
• Can you describe Lockheed Martin's approach to innovation in AI technologies?
• What opportunities for professional development does Lockheed Martin offer to interns?
• How does the team integrate feedback into the project development cycle?
• What are the biggest challenges the team is currently facing?
• What qualities do you value most in team members at Lockheed Martin?
• How does Lockheed Martin foster diversity and inclusion within its teams?
• What are the next steps in the development of AI technologies at Lockheed Martin?

Study topics (8)

• Python Programming for AI

  Study Python fundamentals, data structures, object-oriented programming, and AI-specific libraries like NumPy, SciPy, and scikit-learn. Practice coding exercises focused on data manipulation and basic ML models. This is essential as the role requires experience developing in Python focused on AI, which is a basic qualification for Lockheed Martin AI intern positions.

• Pandas and Matplotlib for Data Handling and Visualization

  Learn Pandas for data analysis, cleaning, and manipulation, and Matplotlib for plotting and visualizing datasets. Build projects that load, process, and visualize sample AI datasets. These tools are explicitly mentioned in job postings for handling data in AI/ML engineering tasks at Lockheed Martin.

• Core Machine Learning Algorithms

  Review supervised/unsupervised learning, regression, classification, clustering, and evaluation metrics. Implement algorithms using scikit-learn on real datasets. Familiarity with algorithms is a required qualification, critical for AI platform development and prototyping at Lockheed Martin.

• Vector Calculus and Advanced Math for AI

  Study vector calculus, gradients, partial derivatives, and matrix operations relevant to ML optimization like gradient descent. Understand applications in neural networks and backpropagation. This is listed as a basic or desired skill in Lockheed Martin AI/ML intern roles, foundational for advanced AI concepts.

• C++ Programming Basics

  Cover C++ syntax, pointers, memory management, STL, and performance optimization for AI applications. Compare with Python for hybrid development. Job postings mention coding ability in C++ as basic/desired, useful for high-performance AI systems in defense tech.

• Large Language Models (LLMs) Fundamentals

  Explore LLM architecture, transformers, fine-tuning, and inference using libraries like Hugging Face Transformers. Review prompt engineering and ethical considerations. Desired skill in Lockheed Martin postings, increasingly relevant for AI platform engineering in modern applications.

• Software Development Lifecycle and Agile Practices

  Learn requirements gathering, implementation, integration, testing, and deployment in AI projects; study Agile sprints and prototyping. Understand version control with Git. Critical for the role's focus on full lifecycle development, rapid prototyping, and team collaboration at Lockheed Martin.

• AI Ethics, Security, and Defense Applications

  Study AI bias mitigation, secure AI deployment, and applications in autonomous systems or defense tech. Review Lockheed Martin's values like problem-solving and strategic thinking. Important for aligning with company culture and potential security clearance requirements in a defense contractor setting.