About the Role: We are seeking a versatile Software Engineer who is passionate about seeing their work realized in end-to-end products used to design Qualcomm chips. The ideal candidate will be responsible for developing and integrating machine learning models, software components, and deployment infrastructure. This role involves creating robust training and inference pipelines, ensuring seamless integration into design processes, and maintaining enablement tooling and infrastructure for efficient machine learning operations.

Key Responsibilities:

• Develop and optimize training and inference pipelines for machine learning models.

• Translate prototype and research code into scalable, production-grade models.

• Integrate various software components into a cohesive, production-quality end-to-end flow.

• Ensure the integrated flow fits seamlessly into new or existing design processes.

• Implement and maintain monitoring systems to ensure model performance and reliability.

• Develop and maintain deployment and development enablement tooling and infrastructure.

• Manage compute resources, disk storage, and environments/containers.

• Implement and oversee release and regression processes.

• Design, build, and maintain CI/CD pipelines to streamline development and deployment.

• Perform regression tests on a fixed test suite to verify integration correctness.

• Collaborate with cross-functional teams to ensure smooth integration and functionality.

• Troubleshoot and resolve integration and deployment issues promptly.

• Create comprehensive component usage instructions and documentation.

• Continuously improve integration and deployment processes and methodologies.

Required Skills and Qualifications:

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

• Proven experience in machine learning, software engineering, MLOps, or a related role.

• Proficiency in programming languages such as Python, Java, or C++.

• Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn).

• Strong understanding of software development principles and best practices.

• Experience with containerization technologies (e.g., Docker, Kubernetes).

• Familiarity with CI/CD pipelines and tools (e.g., Jenkins, GitLab CI).

• Experience with regression testing and test suite management.

• Excellent problem-solving skills and attention to detail.

• Strong communication skills and ability to work collaboratively in a team environment.

Preferred Qualifications:

• Experience with cloud platforms (e.g., AWS, Azure, Google Cloud).

• Knowledge of infrastructure as code (IaC) tools (e.g., Terraform, Ansible).

• Familiarity with monitoring, logging and visualization tools (e.g., Prometheus, Grafana).

• Knowledge of data engineering and data pipeline development.

• Familiarity with DevOps practices and tools.