Profile, benchmark, and optimize performance across embedded hardware products. Define KPIs, build tooling, and drive optimizations from lab through launch under tight latency, power, and memory constraints.
293k – 325k
Hybrid7+ YOEEmbedded Engineering
About the role
Responsibilities
Develop system performance benchmarks, methodologies, and policies to evaluate end-to-end product behavior.
Profile and analyze performance across key product use cases and workloads using custom and industry-standard profiling tools.
Partner closely with engineering teams to identify bottlenecks and drive performance optimizations across the software stack.
Define high-level product KPIs and establish measurement frameworks to measure launch readiness and monitor performance throughout the product lifecycle.
Measure, report, and track metrics including system responsiveness, memory utilization, cold boot performance, long-term stability, and behavior under constrained conditions such as thermal limits and low battery states.
Investigate field performance regressions and drive issues to resolution using data-driven analysis.
Requirements
Deep experience shipping embedded or high-performance systems on Linux, Android, or iOS devices.
Strong proficiency in C/C++, Python, or Rust, with exceptional operating systems fundamentals, including process management, IPC, drivers, interrupts, power management, media frameworks, graphics, filesystems, and networking.
A proven track record shipping real-time systems operating under tight latency, power, and memory constraints.
Expertise with profiling and debugging tools such as Perfetto, GDB, LLDB, perf, ftrace, or Instruments.
Experience building instrumentation, analytics, and performance tuning frameworks.
Nice-to-Haves
Familiarity with statistics, experimentation methodologies, or data science concepts.
Experience collaborating cross-functionally with embedded software, systems, camera and vision, audio, graphics, applications, and hardware teams.
Experience defining product KPIs, benchmarking methodologies, and launch readiness metrics for hardware products.
Experience investigating field performance regressions and driving fixes through cross-functional collaboration.
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