AI engineer - Distributed Systems

As an AI Engineer specializing in Distributed Systems at Luma AI, your responsibilities will include working closely with the research team on experiment tracking and tooling, automating Python environments, maintaining CI/CD pipelines, and defining test suites for cluster stability. You will be instrumental in ensuring our training runs are efficient and reproducible while troubleshooting system issues.

To qualify for the AI Engineer - Distributed Systems role at Luma AI, you should have excellent software engineering skills, particularly in maintaining and working with typed and tested PyTorch codebases. Additionally, experience with Slurm, GitHub CI/CD, and Docker is essential. Strong problem-solving skills and the ability to work collaboratively with a team are also important.

The technology stack for the AI Engineer - Distributed Systems at Luma AI primarily involves Python, PyTorch, Docker, and Slurm, among other tools. Experience with GitHub CI/CD is also critical for setting up and maintaining automated testing and deployment pipelines.

At Luma AI, the culture is collaborative and product-focused. Engineers work alongside researchers and designers to create innovative solutions, making it a great place for those who thrive in a team-oriented environment. The company values creativity and encourages team members to contribute to building and shipping products that make a difference.

As an AI Engineer - Distributed Systems at Luma AI, you will have numerous opportunities for growth. You’ll be part of a pioneering team that’s at the forefront of AI technology. The learning environment is dynamic, and your contributions will directly influence the development of multimodal AI systems, which can pave the way for advancement in your career.

When answering this question, focus on specific projects or tasks where you've applied PyTorch. Highlight your understanding of how to manage typed and tested codebases, and how your experience can contribute to Luma AI's objectives in developing systems that are not only effective but innovative.

A good approach is to explain your systematic method for identifying the root cause of issues, such as using logging tools, monitoring system performance, and testing different components in isolation. This showcases your problem-solving skills crucial for the role.

Discuss your familiarity with CI/CD principles and specific tools you've used, explaining how you would tailor these implementations to suit Luma AI's distributed ML workloads. Mention the importance of automated testing to ensure codebase stability.

Share strategies such as hyperparameter tuning, model architecture selection, and leveraging distributed computing resources. Emphasize adaptability based on the specific requirements of projects you'll be working on at Luma AI.

Explain your practices for writing clean, modular code, such as adhering to coding standards, using version control, and writing unit tests. Mention any tools you utilize to maintain code quality over time.

Share a specific scenario that emphasizes your problem-solving skills, teamwork, and technical expertise. Focus on your contributions and what you learned from the experience.

Discuss the resources you rely on for continuous learning, such as research papers, online courses, and professional networks. Emphasize your dedication to staying updated on trends that will impact your work at Luma AI.

Detail projects where you’ve used Docker, focusing on how it facilitated creating reproducible environments for experiments and how it can help standardize deployments in Luma AI's distributed systems.

Discuss your process for analyzing results, examining potential flaws in methodology, and executing necessary adjustments. Highlight your analytical mindset and resilience in research settings.

Share your passion for AI and how Luma AI's vision of merging AI models with delightful products aligns with your career goals. Speak about the excitement of being part of a team working on groundbreaking technology.