Sr. Edge AI Firmware Engineer Austin, Texas

Ambiq's mission is to enable intelligence everywhere by delivering the lowest power semiconductor solutions. Ambiq is a pioneer and a leading provider of ultra‑low‑power semiconductor solutions based on our proprietary and patented sub‑ and near‑threshold technologies. With the increasing power requirements of artificial intelligence (AI) computing, our customers are relying on our solutions to deliver AI to edge environments. Our hardware and software innovations fundamentally deliver a multi‑fold improvement in power consumption over traditional semiconductor designs without expensive process geometry scaling.

We began in 2010 by addressing the power consumption challenges of battery‑powered devices at the edge, where they were most pronounced. As of the beginning of 2025, we’ve shipped more than 2 800+ million units worldwide.

Our innovative and fast‑moving teams of design, research, development, production, marketing, sales, and operations are spread across several continents, including the US (Austin), Taiwan (Hsinchu), China (Shanghai and Shenzhen), and Singapore. We value relentless technology innovation, a deep commitment to customer success, collaborative problem‑solving, and an enthusiastic pursuit of energy efficiency. We embrace candidates who also share these same values. The successful candidate must be self‑motivated, creative, and comfortable learning and driving exciting new technologies.

We encourage and nurture an environment that fosters growth and opportunities to work on complex, meaningful, and challenging projects, creating a lasting impact and shaping the future of technology. Join us on our quest for enabling billions of intelligent devices. The intelligence everywhere revolution starts here.

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 tool chains.

• Develop and support Ambiq’s embedded AI runtimes (Helia
  
  RT—our fork/extension of Tensor Flow Lite for Microcontrollers—and Helia
  
  AOT) 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 Helia
  
  DSP and Helia Core 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 tool chains (bare metal, FreeRTOS, Zephyr).
• Improve memory planning/runtime efficiency and manage upstream/fork health; publish and maintain customer‑facing assets (docs, guides, examples, benchmarks).

• BS in Electrical/Computer Engineering, Computer Science, or related field + 5+ 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…