Senior Firmware Engineer, Edge AI / NPU Runtime

• Edge AI & NPU Inference
• Own deployment of ML models onto embedded targets using NPUs, DSPs, MCUs, or other hardware accelerators.
• Integrate embedded inference runtimes, vendor NPU/DSP SDKs, and model deployment workflows into production firmware.
• Optimize inference latency, memory footprint, throughput, power consumption, and accelerator utilization on production hardware.
• Partner with ML teams on quantization, operator support, model architecture tradeoffs, calibration datasets, and accuracy/performance regressions.
• Realtime Sensor-to-Inference Systems
• Build realtime sensor-to-inference pipelines, including acquisition, timestamping, synchronization, preprocessing, feature extraction, model execution, and postprocessing.
• Design low-latency data movement using DMA, interrupts, ring buffers, deterministic scheduling, and efficient memory layouts.
• Support streaming inference patterns such as sliding windows, temporal models, event-driven execution, and continuous sensor processing.
• Maintain inference quality and timing guarantees under real-world conditions such as sensor noise, clock drift, dropped samples, variable system load, and power-state transitions.
• Power-Optimized Embedded Firmware
• Optimize end-to-end energy per inference across sensing, preprocessing, model execution, postprocessing, and idle time.
• Use low-power firmware techniques such as sleep states, duty cycling, subsystem power gating, clock scaling, batching/windowing, and dynamic power management.
• Profile and improve power consumption across sensors, CPU, NPU/DSP, memory, and supporting firmware infrastructure.
• Product Quality & Debugging
• Bring up and debug firmware across sensors, accelerators, power systems, embedded compute, and production hardware.
• Use lab tools, traces, logs, telemetry, and instrumentation to root-cause complex embedded system issues.
• Translate product and customer experience goals into concrete latency, reliability, responsiveness, and power targets.
• Build diagnostics, validation hooks, and performance benchmarks to ensure reliable real-world edge inference behavior.
• 5+ years of experience in embedded firmware, embedded systems, or edge ML systems.
• Strong C/C++/Rust experience on resource-constrained embedded platforms.
• Experience with RTOS-based systems such as FreeRTOS, Zephyr, ThreadX, or similar.
• Experience deploying or optimizing ML inference on embedded targets, NPUs, DSPs, MCUs, or edge SoCs.
• Strong understanding of realtime embedded systems, including DMA, interrupts, concurrency, memory management, and low-latency data movement.

None flagged.