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Computer Vision Engineer, Senior

9 Mothers - Austin | OnSite

Posted Jun 10, 2026

Benefits

Parental leave
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Non-birth-parent leave
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Family-building benefits
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  • Adoption assistance: Not verified
  • Surrogacy assistance: Not verified
Mental health support
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Relocation assistance
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Childcare support
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Learning budget
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Salary
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401(k) match
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Market context

U.S. role benchmark (BLS OEWS)
$116,543 U.S. median for this role
Projected growth (BLS Employment Projections)
+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function
Engineering From the posting source checked Jun 20, 2026
Seniority
Senior From the posting source checked Jun 20, 2026
Work mode
Onsite From the posting source checked Jun 20, 2026
In-office days
5 days From the posting source checked Jun 20, 2026

Schedule

Shift type
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Weekend work
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Application

Cover letter
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Assessment
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Deadline
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Where they hire

State eligibility is not yet verified.

About this role

Computer Vision Engineer, Senior Austin | OnSite LOCATION: ONSITE - AUSTIN, TX Employment Type: Direct Hire, Full‑Time Job Title: Senior Computer Vision Engineer Company Overview 9 Mothers Defense develops AI-enabled systems to counter unmanned aerial threats. Our first product, EDDA, is an autonomous counter-sUAS point-defense platform designed to detect, track, and neutralize Group 1 drone threats. The company is headquartered in Austin, Texas. Position Summary We are seeking a Senior Computer Vision Engineer to serve as the “eyes” of our autonomous c-sUAS platforms. You will design, implement, and optimize the entire perception pipeline, specializing in low-latency, high-frame-rate processing to track small, fast objects with zero margin for error. You should be comfortable building models and systems from the ground up, moving beyond simply utilizing existing frameworks. Essential Duties - Architect and Implement: Develop the entire embedded CV pipeline using high-performance Python and C++. - Target Tracking & Sensor Fusion: Design and deploy robust multi-object tracking and sensor fusion algorithms to ensure high-fidelity state estimation of fast-moving targets. - Object Detection: Utilize real-time models (e.g., YOLO) and CNNs optimized for speed. - Embedded Optimization: Optimize code for low-latency performance on resource-constrained NVIDIA Jetson environments using libraries like TensorRT and TFLite. - Geometric Vision: Apply principles of calibration, rectification, and 3D geometry to translate 2D footage into accurate 3D coordinates for fire control. - Cross-Functional Collaboration: Work with robotics teams to ensure perception data is reliable for autonomous decision-making. Requirements - Programming: Strong background in C++ (for performance) or Python. - SLAM/Localization:

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