Okay, I've searched for full-time Machine Learning Engineer roles in Raleigh, NC, that require experience with PyTorch and Kubernetes, and include 401(k) benefits. I have found 47 positions for you.

Sure. Here's the analysis:

Job Analysis:

The Machine Learning Engineer - AI Engineering role at Red Hat is fundamentally about bridging cutting-edge AI/ML technology with real-world, scalable software solutions within an open source ecosystem. The core purpose is to design, build, and optimize machine learning models and infrastructure that can be productionized on Red Hat’s platforms such as OpenShift while actively contributing to the broader community and collaborative projects like KServe and Kubeflow. The job demands not only strong technical skills in machine learning frameworks (e.g., PyTorch, scikit-learn), cloud-native technologies (Kubernetes, OpenShift, Docker), and programming (Python, Go), but also the ability to work cross-functionally with data scientists, researchers, and open source developers. Success looks like creating reliable, scalable ML systems that democratize AI technology, improving user understanding of ML predictions, and innovating while embracing open collaboration. Candidates must navigate ambiguity inherent in emerging AI fields and operate with a degree of autonomy, making decisions around architecture, model optimization, and integration often in rapidly evolving environments. Additionally, the role requires agility in adopting new tools and maintaining clear communication within a diverse, global team. While the technical foundation is critical, demonstrating strategic thinking around production-readiness and community engagement differentiates an exceptional candidate.

Company Analysis:

Red Hat is a global leader in enterprise open source software, well-known for its transparent, inclusive, and innovation-driven culture. It occupies a position as a mature yet forward-thinking company positioned at the intersection of cloud, containers, and AI, leveraging community-powered development to remain competitive and relevant. This means the Machine Learning Engineer role sits within an organization that values contributions from all levels, encourages knowledge sharing, and supports flexible working styles including remote options. The open source ethos and collaborative environment suggest a culture where intellectual curiosity, ownership, and diversity of thought are prized, fostering an atmosphere where engineers can meaningfully influence product direction and community projects. This role is probably individual contributor-heavy but with broad cross-team visibility, particularly in product and community development arenas. Importantly, the position aligns with Red Hat’s strategy to lead in enterprise AI by providing scalable, reliable, and accessible ML solutions. Given the company's scale and focus on innovation paired with stable technologies (like Kubernetes and OpenShift), the job offers long-term growth potential for someone wanting to impact open source AI globally while being supported by world-class processes, benefits, and inclusive values.

Absolutely. Here are some mock interview questions that could come up:

• Tell me about a time when you had to optimize a machine learning model for deployment under strict scalability or latency constraints. How did you approach the challenge?
• Describe a situation where you needed to collaborate with non-engineering teams (like data scientists or researchers) to productionize a model. What communication strategies worked for you?
• Can you share an experience where you had to quickly learn and implement a new technology or tool to solve a problem? What was the outcome?
• How do you ensure maintainability and scalability when designing ML infrastructure and pipelines?
• Explain your experience working with Kubernetes, OpenShift, or similar cloud-native platforms in the context of machine learning workflows.
• Walk me through your approach to feature engineering in a recent ML project. How did it impact the model’s performance?
• Given the dual-team setup (InstructLab vs. OpenShift AI), how would you evaluate which environment aligns best with your skills and career aspirations?
• Imagine you notice that a production ML model's predictions are degrading over time. How would you investigate and address this issue?
• How would you contribute to open source AI communities like KServe or Kubeflow while balancing your internal project responsibilities?
• What motivates you to work for a company like Red Hat that champions open source principles? How do you see yourself fitting into this culture?
• Why are you interested in contributing to Red Hat’s AI engineering team specifically, as opposed to other AI roles in the industry?
• How do your personal values align with Red Hat’s commitment to diversity, inclusion, and collaboration?
• What challenges do you anticipate in democratizing AI in the enterprise, and how would you help overcome them at Red Hat?
• Can you describe a past project where your work made a tangible impact on users or the business?
• What questions do you have about the Red Hat AI Engineering team's current priorities or upcoming projects?