Machine Learning Engineer

The Machine Learning Engineer at Hedra is responsible for designing and implementing scalable computing solutions for training and deploying machine learning models. This includes managing and optimizing performance across cloud computing platforms like AWS or Google Cloud, ensuring that the infrastructure can handle large video datasets, and collaborating with the team to meet their computational needs effectively.

Hedra requires a Bachelor's degree in Computer Science, Information Technology, or a related field focused on system administration. Candidates should have experience with cloud computing platforms especially in managing large-scale ML workloads, knowledge of containerization tools like Docker, and proficiency in scripting languages such as Python or Bash for infrastructure management.

A Machine Learning Engineer at Hedra should be well-versed in cloud computing platforms, containerization tools like Docker, orchestration tools such as Kubeflow, and have a strong understanding of distributed training techniques. Proficiency in scripting languages like Python or Bash for automation tasks is also essential for facilitating infrastructure management.

At Hedra, Machine Learning Engineers enjoy competitive compensation and equity, healthcare options including Silver PPO Medical, Vision, and Dental coverage, 401k (no match), along with delicious lunches and snacks at the office. These benefits contribute to a vibrant and supportive work environment.

The Machine Learning Engineer role at Hedra is critical for ensuring that the company's computational backbone effectively supports its extensive ML efforts. This position focuses on optimizing deployment and scalability of generative models, which are essential for innovating in content creation and pushing technological boundaries.

When answering this question, it’s important to highlight specific projects where you've utilized cloud computing platforms like AWS or Google Cloud. Discuss the services you leveraged, such as EC2 for scalable computing or S3 for data storage, and how they contributed to successful ML workloads.

To respond effectively, you might want to mention techniques such as monitoring system metrics and using tools like Kubeflow for orchestration. Discuss specific improvements you implemented in past roles to maximize efficiency, such as load balancing or resource allocation strategies.

In your answer, specify the scripting languages you're comfortable with, notably Python or Bash. Provide examples of automation tasks you've completed, such as setting up CI/CD pipelines or automating data preprocessing steps, and how these contributed to your projects.

Here, you should describe your knowledge of distributed training methods, including data parallelism and model parallelism. Use examples from your previous experience that demonstrate how you managed the scalability of models across multiple GPUs or machines effectively.

When addressing this, emphasize communication skills and your approach to teamwork. Discuss how you ensure alignment with team goals, share knowledge, and facilitate discussions to understand diverse computational needs, fostering a collaborative environment.

Share a specific challenge you encountered, such as resource constraints or slow processing times, and explain how you addressed these issues. Highlight any relevant tools or strategies you employed to overcome the challenges effectively.

In this answer, you can discuss the importance of resource allocation and selecting appropriate infrastructure solutions. Mention how you would use cloud instances to support rapid generation needs and the role of orchestration tools like Kubeflow in this process.

Provide specific examples of how you have used containerization tools such as Docker to deploy ML models in the past. Discuss any experiences with orchestration tools like Kubernetes or Kubeflow, emphasizing how these experiences have contributed to efficient model deployment at scale.

In your response, outline your typical problem-solving steps, from diagnosing the issue to implementing a solution. Provide an example that showcases your analytical skills and how you effectively resolved an infrastructure issue.

Share your passion for innovative technology and how you resonate with Hedra’s mission to transform content creation. Discuss your desire to work in a dynamic environment and your commitment to contributing to pioneering projects that change the industry landscape.