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AI Power Consumption Prediction Engineer

Blake Goldberg

Contract · Job · Los_Angeles

About the role

A wearable device with a 2-day battery life is a fashion accessory. A wearable with a 2-week battery life is a medical device. The difference comes down to how aggressively the firmware can duty-cycle the sensors and compute — and that requires accurate real-time predictions of what each component will consume under different usage patterns. We build AI for industrial and medical wearables. Power consumption prediction is one of our most important modeling challenges: given the current device state, user activity, and scheduled tasks, predict power draw over the next 30 minutes with enough accuracy to make intelligent throttling decisions. The model runs on-device at 10Hz, so it needs to be tiny and fast. This is a 9-month contract to develop the power prediction model and its on-device deployment pipeline. You'll work with firmware engineers who understand the hardware intimately and data scientists who've collected the training dataset from 8 months of field deployment across 1,200 devices.

Responsibilities

  • Develop power consumption prediction models running on ARM Cortex-M hardware
  • Optimize model size and inference speed for on-device deployment at 10Hz
  • Build training pipeline from field-collected device telemetry data
  • Implement continuous model update pipeline as new device firmware is released
  • Validate prediction accuracy against real device power measurements
  • Collaborate with firmware engineers on runtime integration and throttling logic

Requirements

  • 5+ years in ML engineering with embedded or IoT deployment experience
  • Experience with model compression: quantization, pruning, or knowledge distillation
  • Strong Python and PyTorch skills
  • Understanding of time series regression and online learning approaches
  • Familiarity with ARM hardware constraints and embedded inference runtimes

Benefits

  • Fully remote contract
  • Premium senior engineering rate
  • Access to field-collected device dataset
  • Collaborative firmware team
  • Strong conversion potential

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Categories

AI & Machine Learning Engineers MLOps & AI Infrastructure

Skills

python pytorch statistics pandas docker

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Job Type

Contract

Level

Senior

Language

English

Salary Range

$130k – $160k / year

AI Expertise

AI & Machine Learning Engineers MLOps & AI Infrastructure

Skills

python pytorch statistics pandas docker aws

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