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Perception Engineer, Staff

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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Verification
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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
Staff Plus 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
Not stated

Where they hire

State eligibility is not yet verified.

About this role

Perception Engineer, Staff Austin | OnSite Location: Onsite - Austin, TX Employment Type: Direct Hire, Full‑Time Job Title: Perception Lead, Staff Eng 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 9 Mothers is seeking a Perception Lead to own the computer vision and machine learning stack that powers target detection, classification, and tracking in our counter-sUAS systems. The Perception Lead is responsible for the research, development, and deployment of the perception pipeline that enables EDDA to engage aerial threats in real time. This is a senior individual contributor position. Essential Duties - Own the visual perception pipeline end-to-end, including detection, classification, and tracking of sUAS targets in real time. - Design and train machine learning models that meet latency and accuracy requirements for edge deployment on Jetson-class hardware. - Architect and maintain the dataset and simulation pipeline, including data collection, labeling, curation, augmentation, synthetic data generation, and closed-loop retraining based on field performance. - Optimize inference performance on Jetson platforms, including model pruning, quantization, TensorRT integration, and custom kernel development as required. - Establish the model evaluation framework and metrics used to assess perception performance under operational conditions. - Deliver target state information (position, velocity, identity, uncertainty) to the controls subsystem. - Collaborate with the hardware team on camera, optics, and sensor selection. Requirements - 6+

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