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AI Wearable Firmware Engineer

Blake Goldberg

Full-time · Job · Chicago

About the role

Running a seizure detection model on a watch with 256KB of RAM and a 100MHz processor sounds impossible. We do it — and the model needs to run continuously for 7 days on a single battery charge while maintaining clinical-grade sensitivity. This is the extreme end of ML engineering: making powerful models work within constraints most engineers never encounter. You'll optimize neural network architectures for wearable deployment: quantization strategies that preserve accuracy at 4-bit precision, architecture search finding the smallest models that meet clinical performance thresholds, and the firmware integration that runs inference on bare-metal processors without an operating system. Every kilobyte of model size matters. Every milliwatt of inference power consumption affects battery life. Every false negative is a missed seizure. You'll navigate these tradeoffs daily, making engineering decisions that directly affect patient safety and device usability.

Responsibilities

  • Optimize neural networks for sub-256KB wearable deployment
  • Design quantization strategies preserving clinical accuracy at 4-bit precision
  • Run architecture search for minimal models meeting clinical performance thresholds
  • Integrate inference engines with bare-metal firmware on low-power processors
  • Balance model accuracy against battery life and computational constraints

Requirements

  • 6+ years in ML engineering with embedded or edge deployment experience
  • Deep PyTorch and TensorFlow skills for model optimization
  • Experience with model quantization and neural architecture search
  • Understanding of embedded systems and power consumption optimization
  • Track record deploying ML on resource-constrained hardware

Benefits

  • Fully remote — US time zones
  • Senior equity package
  • $4,000 annual conference budget
  • Engineering at the frontier of wearable AI

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Categories

AI & Machine Learning Engineers

Skills

python pytorch tensorflow docker aws

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Level

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Language

English

Salary Range

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AI Expertise

AI & Machine Learning Engineers

Skills

python pytorch tensorflow docker aws

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