Defense Perception Engineer — Edge ML & Real-Time Vision

Applied Intuition

Sunnyvale (CA)

On-site

USD 160,000 - 220,000

Full time

10 days ago
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Job summary

Applied Intuition Defense seeks a Perception Engineer to advance end-to-end perception systems across maritime, land, air-to-surface and more, deploying models to edge devices and integrating with DoD testing. The role emphasizes hands-on development, large-scale data handling, and collaboration with elite AI talent from Applied Intuition’s self-driving division.

The candidate will work with transformer-based models, vision-language and diffusion techniques, building tools and pipelines for

Qualifications

  • Proficiency in training ML models in PyTorch on multi-machine, multi-GPU systems.
  • Experience deploying ML models to edge devices.
  • Strong coding in Python and/or C++.

Responsibilities

  • Develop perception models that find and classify targets with high accuracy.
  • Deploy perception models to Jetson Orin+ boards and optimize real-time performance.
  • Build tooling across the perception stack: labeling tools, simulation.
  • Collaborate with AI talent across Applied Intuition Defense and self-driving teams.
  • Scale datasets using simulation, diffusion, and generative AI techniques.
  • Create optimized inference software for high FPS on Jetson-like hardware.
  • Coordinate with hardware, sensor, and autonomy teams for DoD testing deployments.

Skills

PyTorch
Python
C++
Edge deployment
Multi-machine multi-GPU
Transformer models

Education

MS or PhD in Computer Engineering, Robotic Engineering, Computer Science

Tools

GitHub Copilot
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Job description

Applied Intuition Defense seeks a Perception Engineer to advance end-to-end perception systems across maritime, land, air-to-surface and more, deploying models to edge devices and integrating with DoD testing. The role emphasizes hands-on development, large-scale data handling, and collaboration with elite AI talent from Applied Intuition’s self-driving division.

The candidate will work with transformer-based models, vision-language and diffusion techniques, building tools and pipelines for

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