Senior Firmware Engineer, Edge AI / NPU Runtime

We’re looking for a Senior Firmware Engineer, Edge AI / NPU Runtime to help architect, optimize, and ship next-generation neurotech hardware with production-grade on-device intelligence. You will own critical parts of the embedded AI stack, from realtime sensor acquisition through preprocessing, NPU/DSP-accelerated inference, postprocessing, telemetry, and product deployment.

This is a hands-on role for someone who wants to work close to the hardware while shaping the intelligence users experience in the product. You’ll help define how models run on-device, how sensor data moves through the system, and how we meet tight latency, reliability, and power budgets in real-world use.

What you’ll do

• 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.

Do you have experience in System performance optimization?, * 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.
• Experience optimizing embedded systems for latency, memory footprint, throughput, and power consumption.
• Hands-on debugging and bring-up experience across embedded hardware and firmware systems, with strong cross-functional communication across firmware, ML, electrical, software, and product teams., * Experience with embedded inference runtimes, deployment toolchains, or edge AI SoCs/accelerators such as TensorFlow Lite Micro, ONNX Runtime, CMSIS-NN, Qualcomm QNN/SNPE, ARM Ethos-U/Vela, TVM, ExecuTorch, Qualcomm, ARM, Cadence/Tensilica, Syntiant, Ambiq, Nordic, NXP, ST, Hailo, Google Edge TPU, or similar.
• Experience with quantized inference, fixed-point math, SIMD/DSP optimization, accelerator programming, or model conversion workflows.
• Experience with streaming or time-series ML workloads such as biosignals, sensor fusion, audio, gesture recognition, keyword spotting, or other realtime inference systems.
• Experience shipping battery-powered consumer electronics, wearable, neurotech, AR/VR, robotics, camera, IoT, or other embedded AI products.

2.32.3 out of 5 stars San Francisco, CA $150,000 - $200,000 a year - Full-time, Pulled from the full job description

• Health insurance
• 401(k) matching
• Vision insurance
• 401(k) 3% Match
• Dental insurance
• Unlimited paid time off
• Visa sponsorship, * Competitive equity package
• Comprehensive medical, dental, and vision insurance
• Company size: 20-30 people
• Unlimited PTO
• Visa sponsorship
• 3% 401k matching

Compensation Range: $150K - $200K