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

Amazon Amazon · Big Tech · Cupertino, CA · Software Development

Senior Firmware Engineer role focused on developing firmware algorithms for power and performance management on ML Acceleration Chips. The role involves designing and implementing intelligent control algorithms, optimization strategies, and real-time decision-making systems, with a focus on embedded, resource-constrained environments and close hardware interaction. Experience with instrumentation, tracing, and data pipelines for algorithm validation is also required.

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
  • 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
  • Understanding of algorithmic complexity and optimization techniques for embedded systems
  • Familiarity with hardware performance counters, on-chip monitoring, or hardware debug interfaces
  • Experience with data collection pipelines and scripting (Python, shell) for algorithm validation
  • Understanding of ML training/inference workloads and their performance characteristics
  • Takes strong ownership, works effectively in ambiguous situations, demonstrates a bias for action while consistently delivering impactful results

What the JD emphasized

  • firmware algorithms for power and performance management
  • ML Acceleration Chips
  • real-time decision-making systems
  • ML training/inference workloads

Other signals

  • ML Acceleration Chips
  • firmware algorithms for power and performance management
  • real-time decision-making systems
  • ML training/inference workloads