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63 Shield Ai Firmware Engineer Jobs Hiring Near You

Qualitest Group

Firmware Engineer

Santa Clara, CA · On-site

Are you interested in working with the World's leading AI-powered Quality Engineering Company ... Role - Firmware Engineer Location - Ballground, GA (onsite) Top Must have: 1. Bachelor's degree in ...

Monaco Enterprises Inc

Firmware Engineer

Spokane, WA · On-site

$80K - $101K/yr

Candidates should possess a willingness to utilize Artificial Intelligence (AI) assistance and tooling to support firmware development and problem-solving within the Firmware Engineering lifecycle.

Monaco Enterprises Inc

Firmware Engineer

Spokane, WA · On-site

$80K - $101K/yr

Candidates should possess a willingness to utilize Artificial Intelligence (AI) assistance and tooling to support firmware development and problem-solving within the Firmware Engineering lifecycle.

Shield AI

Engineer II, Electrical (R5111)

Dallas, TX · On-site

$90K - $130K/yr

Founded in 2015, Shield AI is a venture-backed defense-tech company with the mission of protecting ... Work closely with Firmware to integrate applicable logic designs into the build/release cycle

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Infographic showing various Firmware Engineer job openings at Shield Ai in the United States as of July 2026, with employment types broken down into 98% Full Time, and 2% Part Time. Highlights an 97% Physical, 1% Hybrid, and 2% Remote job distribution.

Principal Edge AI Firmware Engineer

Ambiq Micro, Inc.

Austin, TX

Other

Re-posted 6 days ago

Job description

Scope

Ambiq is seeking an Edge AI Firmware Engineer to build and optimize the embedded software stack that powers real-time, battery-powered on-device AI. You will develop Ambiq's AI runtimes, performance-critical operator libraries, and profiling/debug tooling that enable customers to deploy efficient inference on resource-constrained devices.

While the cloud has been the default home for AI, the next frontier is distributing intelligence everywhere-directly onto real-world devices. Edge AI enables real-time responsiveness, stronger privacy, lower bandwidth cost, and reliable operation even without connectivity. This role helps accelerate the shift to on-device intelligence across a rapidly growing ecosystem of health and fitness wearables, smart glasses, industrial IoT, and always-on sensors.

You'll work closely with platform, silicon, and applied ML teams-and directly with customers-to ensure our software extracts maximum performance and energy efficiency from Ambiq hardware while remaining easy to integrate into production embedded toolchains.

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 tools that convert 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).

Qualifications 

  • BS in Electrical/Computer Engineering, Computer Science, or related field + 12+ years relevant experience (or equivalent practical experience). MS is a plus, especially in embedded systems, compilers, computer architecture, or ML systems.
  • Strong experience designing, developing, and testing embedded software in C/C++.
  • Strong debugging discipline with an emphasis on correctness, reproducibility, and performance regression prevention.
  • Solid understanding of compute, memory, and bandwidth/cache effects on deterministic latency and energy efficiency in constrained systems.
  • Ability to interpret hardware/software documentation (datasheets, reference manuals; schematics a plus).
  • Proficiency with Git (or equivalent version control).
  • Efficient use of AI-assisted development tools to improve productivity while maintaining engineering rigor.
  • Experience with embedded development workflows: Arm toolchains, GDB + J-Link/SEGGER-class probes, and CMake/Make build systems.

Nice to have:

  • Python for profiling/analysis tooling, automation, and developer utilities.
  • Rust experience.
  • Familiarity with TensorFlow Lite for Microcontrollers (or similar embedded inference runtimes).
  • Experience optimizing for embedded acceleration targets (e.g., DSP, vector extensions, NPU).
  • Familiarity with CMSIS-NN, CMSIS-DSP, and/or Vela (or similar).
  • Working knowledge of quantized inference (e.g., int8, per-channel) and embedded debugging implications.
  • Packaging/integration (e.g., CMSIS-Pack/CPM.cmake) and/or lab measurement experience (power/debug with basic bench tools).

Must be currently authorized to work in the United States for any employer. 

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