As a Software Engineer specializing in Machine Learning Infrastructure at Character.AI, your main responsibilities include providing vital infrastructure support to our ML research efforts, building tools for diagnosing any cluster issues or hardware failures, and monitoring deployments to manage experiments efficiently. You'll play a crucial role in maximizing GPU allocation and utilization, which is essential for both serving and training processes.

To qualify for the Software Engineer, Machine Learning Infrastructure position at Character.AI, candidates should have over 4 years of experience supporting ML infrastructure. It's essential to have experience in developing diagnostic tools and working with cloud platforms. Familiarity with GPU technologies and high-performance computing environments, especially regarding large language model training, will enhance your application.

In this role at Character.AI, you should be well-versed in cloud computing platforms like Compute Engine, Kubernetes, and Cloud Storage. Experience with GPUs, particularly in high-performance computing, is highly advantageous. Familiarity with machine learning frameworks like PyTorch, TensorFlow, and JAX, along with knowledge of GPU kernel development, is also valuable.

Experience with large GPU clusters is quite important for the Software Engineer, Machine Learning Infrastructure role at Character.AI, as it helps streamline the handling of extensive ML operations. Comfortable managing resources in high-performance computing setups will allow you to optimize ML model training and deployment efficiently.

At Character.AI, there are ample career growth opportunities for a Software Engineer focused on Machine Learning Infrastructure. Working with cutting-edge technology and innovative projects allows for continuous learning and development. Employees can take on leadership roles, specialize further in ML technologies, or explore opportunities in product development, research, and engineering management within the rapidly evolving AI landscape.

In your response, emphasize previous projects where you maintained or improved ML infrastructure, detailing the challenges you faced and the tools or strategies you employed to address them.

Discuss a structured approach to problem-solving, perhaps involving logging, monitoring tools, or systematic troubleshooting processes. Share specific examples of past experiences for added context.

Mention the specific tools you have experience with, such as Kubernetes for orchestration, monitoring dashboards, or custom scripts, and explain how they improved your workflow and efficiency.

Explain strategies like load balancing, optimizing task scheduling, and resource allocation adjustments, referring to tools or frameworks that facilitate these processes.

Share your experience working with specific cloud platforms like Google Cloud or AWS, highlighting what you like about them and how they support ML infrastructure solutions.

Describe any past experience with large language models, your responsibilities in that context, and what you learned from the process, especially regarding infrastructure requirements.

Discuss your approach to redundancy, error handling protocols, and any protocols you have in place to minimize disruption during hardware failures.

Identify the languages you are proficient in, such as Python, along with ML frameworks like TensorFlow or PyTorch. Provide examples of projects where these skills were applied.

Talk about specific projects where you developed or optimized GPU kernels, the challenges faced, and the performance enhancements achieved as a result.

Articulate your motivations honestly—whether driven by the innovative work in AI, the company's rapid growth, or your desire to contribute to meaningful technology. It helps to connect your skills with their mission.