Amazon Senior Software Engineer – Power Performance Machine Learning Acceleration 

United States, California, Cupertino 

845156952

This is a software engineering-focused role centered on building robust frameworks, automating system-level testing, analyzing large datasets, and enabling deep insights into power and performance behavior across distributed workloads.Key job responsibilities
- Develop and implement software and firmware for managing power, thermal, and performance behavior
- Develop and maintain tools to collect, process, and analyze power and performance data for machine learning workloads at scale.
- Develop low-level software interfaces to retrieve metrics from firmware, hardware, and telemetry sources.- Create dashboards and data pipelines to visualize trends, detect anomalies, and enable data-driven decisions.- Continuously evolve infrastructure to improve efficiency, accuracy, and test coverage.
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.About AWSWork/Life Balance
Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

- Bachelor's or Master’s degree in Computer Science, Electrical Engineering, or related field.
- 5+ years of experience in software development using Python, C/C++, or similar languages.
- Proven experience in building tools and automation for system-level testing and data collection.
- Strong foundation in data analysis, including experience with frameworks like Pandas, NumPy, and visualization libraries.
- Experience scaling test infrastructure or automation across large-scale environments (e.g., cloud or data centers).

- Experience developing performance or power telemetry infrastructure.
- Proficiency in dashboard tools such as Grafana, Power BI, or Tableau.
- Familiarity with machine learning workloads (e.g., PyTorch, TensorFlow).
- Strong debugging skills and ability to interpret system-level behavior from raw telemetry.