Responsibilities

• Develop and support Ambiq’s embedded AI runtimes (HeliaRT—our fork/extension of TensorFlow Lite for Microcontrollers—and HeliaAOT) with focus on portability, correctness, performance, and usability.
• Implement and optimize ML operator kernels and embedded libraries for on-chip acceleration (DSP, vector, NPU), including HeliaDSP and HeliaCore components.
• Build and maintain on-device profiling and performance analysis tools, including converting PMU counters into actionable insights.
• Drive improvements in latency, memory footprint, and energy (e.g., joules/inference) through compute/bandwidth and memory-hierarchy analysis.
• Develop benchmark harnesses, microbenchmarks, and regression tests to ensure numerical correctness and prevent performance regressions.
• Enable seamless customer integration across embedded environments and toolchains (bare metal, FreeRTOS, Zephyr).
• Improve memory planning/runtime efficiency and manage upstream/fork health; publish and maintain customer-facing assets (docs, guides, examples, benchmarks).

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