Sr. Firmware Engineer, Annapurna Labs, Machine Learning Acceleration - Power and Performance

Amazon Amazon · Big Tech · Austin, TX · Software Development

This role focuses on developing firmware for power and performance management on ML acceleration chips. The engineer will design and implement intelligent control algorithms, optimization strategies, and real-time decision-making systems, working closely with hardware architects and focusing on resource-constrained environments. The role involves building instrumentation and tracing capabilities for algorithm development and validation.

What you'd actually do

  1. Design and implement firmware algorithms for power management, thermal control, and performance optimization on ML acceleration hardware
  2. Develop real-time control policies and state machines that dynamically balance power, thermal, and performance constraints
  3. Create optimization algorithms for resource allocation, frequency/voltage scaling, and workload scheduling
  4. Implement efficient data structures and algorithms suitable for embedded, resource-constrained environments
  5. Design and implement on-device tracing and telemetry collection systems to support algorithm development and validation

Skills

Required

  • 5+ years of non-internship professional software development experience
  • Experience as a mentor, tech lead or leading an engineering team
  • Bachelor's degree in computer science, electrical engineering, or related field
  • Strong firmware or embedded systems development experience
  • Proficiency in C/C++ for systems programming with strong foundation in algorithms and data structures
  • Experience implementing efficient algorithms in resource-constrained, real-time environments
  • Experience with hardware interfaces, instrumentation, or performance monitoring
  • Strong debugging skills with hardware-software systems
  • Experience building developer tools or instrumentation frameworks

Nice to have

  • Experience developing control algorithms, optimization algorithms, or state machines in firmware
  • Experience with power management algorithms, thermal control policies, or dynamic performance optimization
  • Background in tracing frameworks, telemetry systems, or performance analysis

What the JD emphasized

  • firmware algorithms for power and performance management
  • ML Acceleration Chips
  • real-time decision-making systems
  • resource-constrained environments
  • instrumentation and tracing capabilities