Software Engineer (Intern)

As a Software Engineer Intern at Gimlet Labs, your primary responsibilities will include building, deploying, and scaling AI systems for production use. You will also evaluate and implement cutting-edge AI research, and explore ways to improve both the accuracy and efficiency of AI models. This role requires you to collaborate on our novel compilation framework that organizes and partitions AI workloads across diverse hardware environments, ultimately contributing to a seamless development experience.

To qualify for the Software Engineer Intern position at Gimlet Labs, candidates should be currently pursuing a degree in computer science, engineering, or a related field. A background in AI/ML or distributed systems is essential, and experience with frameworks such as PyTorch, TensorFlow, or ONNX is preferred. Candidates should also be familiar with distributed systems management tools like Kubernetes and have programming skills in Python and C++.

To prepare for the Software Engineer Intern role at Gimlet Labs, candidates should focus on strengthening their knowledge of AI techniques, distributed systems, and scaling applications. Gaining hands-on experience with AI frameworks like PyTorch and TensorFlow can greatly benefit you. Additionally, familiarize yourself with orchestration frameworks such as Kubernetes and dive into the latest AI research to understand current trends in model optimization and performance enhancements.

Gimlet Labs utilizes a range of advanced technologies for AI workload management, centered around an integrated hardware-software stack designed for high efficiency. The team works extensively with AI frameworks like PyTorch and TensorFlow, alongside orchestration tools such as Kubernetes, to optimally manage and monitor AI workloads at production scale, ensuring that systems can handle millions of requests per second.

Gimlet Labs fosters an innovative, collaborative, and inclusive culture where Software Engineer Interns are encouraged to learn, share ideas, and contribute meaningfully to the team. Interns work alongside experienced professionals who value mentorship and development, creating an environment where you can grow your skills while making impactful contributions to groundbreaking AI technologies.

When answering this question, be specific about the projects you've worked on involving these frameworks. Highlight any relevant coursework, personal projects, or internships where you utilized these technologies, detailing your role in implementing, training, and optimizing models to showcase your hands-on experience.

In your response, provide a succinct definition of distributed systems and mention their key characteristics, such as scalability, fault tolerance, and resource sharing. Offer examples of use cases and discuss how they are relevant to the work at Gimlet Labs, emphasizing aspects like workload partitioning and orchestration.

When asked this question, choose a specific project that relates to AI or software engineering. Explain the challenge clearly and discuss the steps you took to address it, including collaborating with team members, thinking creatively, and applying any technical skills you have. This shows not only your problem-solving ability but also your teamwork skills.

Respond by listing the programming languages you are proficient in, such as Python and C++, and explain your experience with them. Discuss how you've utilized these languages in AI projects or other relevant experiences, emphasizing their significance in building scalable systems and handling data effectively.

Demonstrate your enthusiasm for the field by mentioning specific online courses, websites, or communities you follow to stay informed about advancements in AI. You might also mention attending conferences, webinars, or participating in forums where you can learn new techniques and connect with other professionals in the space.

In your answer, emphasize that model accuracy directly affects the effectiveness of AI systems, impacting their reliability and performance in real-world applications. You could discuss the trade-offs between accuracy, efficiency, and speed, and share examples of how you have worked to improve model accuracy in your projects.

Share any relevant experiences you have with deploying machine learning models. Highlight specific challenges such as scaling issues or integration with existing systems, and explain how you addressed these obstacles. This demonstrates your practical experience and understanding of the complexities involved in production deployments.

Discuss the role of orchestration as a critical component in managing resource allocation, load balancing, and ensuring fault tolerance within distributed AI systems. You may want to touch on specific tools like Kubernetes and how they help streamline workflow processes for AI workloads.

Choose an experience where you effectively adapted to a new technology or tool, describing your approach to learning it rapidly—whether through online resources, peer collaboration, or hands-on experimentation. Reflect on how this ability to learn quickly has served you well in your academic or project work, particularly in AI or software development.

Outline a systematic approach to optimizing an AI model, which might include collecting and analyzing data, conducting hyperparameter tuning, or employing techniques like transfer learning. Share any relevant experiences or methodologies you've employed to enhance model performance, reinforcing your understanding of the optimization process.