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AI Embedded Systems ML Engineer

Blake Goldberg

Full-time · Job · Los_Angeles

About the role

PyTorch models that run perfectly on A100 GPUs are useless on an ARM Cortex-M7 with 512KB of RAM. If you understand why — and more importantly, how to fix it — this role is for you. We build AI-powered wearable devices for industrial safety: hard hats that detect fatigue, vests that monitor posture, gloves that predict repetitive strain injuries. The ML challenge is extreme compression. Our models need to run at 30fps on microcontrollers that cost $3 and draw 50mW. You'll take research models from our science team, quantize them, prune them, distill them, and sometimes rewrite them entirely in a framework that supports on-device inference. Then you'll validate that the compressed model still meets our accuracy thresholds in real-world conditions — not just on benchmarks. You'll split time between our San Jose lab (for hardware testing) and remote work. The team is eight engineers across firmware, ML, and cloud, and you'll sit at the intersection of all three.

Responsibilities

  • Compress and optimize ML models for deployment on resource-constrained devices
  • Implement quantization, pruning, and knowledge distillation techniques
  • Validate model accuracy on hardware-in-the-loop test rigs
  • Collaborate with firmware engineers on inference runtime integration
  • Benchmark power consumption and latency against product requirements
  • Maintain a library of optimized model architectures for different device targets

Requirements

  • 3+ years in ML engineering with experience in model optimization
  • Hands-on work with TensorFlow Lite, ONNX Runtime, or similar edge frameworks
  • Understanding of quantization techniques and their accuracy trade-offs
  • Familiarity with embedded systems constraints: memory, power, latency
  • Strong C/C++ skills for performance-critical code paths

Benefits

  • Hybrid role — 2 days in San Jose lab, 3 days remote
  • Health and dental coverage from day one
  • Annual hardware innovation budget of $2,000
  • Patent bonus program
  • Flexible hours around core 10am-3pm PT window

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Categories

AI & Machine Learning Engineers MLOps & AI Infrastructure

Skills

python pytorch tensorflow docker aws

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Job Type

Full-time

Level

Mid-Level

Language

English

Salary Range

$100k – $130k / year

AI Expertise

AI & Machine Learning Engineers MLOps & AI Infrastructure

Skills

python pytorch tensorflow docker aws computer vision

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