Perception Engineer
US$110,000 – US$300,000 / yr base salary
Posted 23 Apr 2026
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- Individual contributor
- Employment
- On-Site
- Contract
- Full-time
| Base salary | US$110,000 – US$300,000 / yr |
|---|
Location: Onsite — Austin, TX
Employment Type: Direct Hire, Full‑Time
Job Title: Perception Engineer
About 9 Mothers
The modern battlefield has changed. Cheap, autonomous suicide drones have turned the tactical advantage upside down, and the world is looking for a solution. At 9 Mothers, we aren’t just "innovating"—we are building the shield.
Backed by top-tier investors, we develop AI powered machines designed to intercept and neutralize Group 1/sUAS threats in real-time. Our flagship product is a low-power, counter-drone system built for the edge—on vehicles, at bases, or in a soldier's pack.
Why 9 Mothers?
While others build for "awareness" or "long-term research," we build for the immediate survival of those in harm’s way. We are a team of hackers, engineers, and mission-driven builders who value field-ready capability over polished slide decks. If you want to see your code or hardware in the field next month—not next year—this is your playground.
Position Summary
9 Mothers is seeking a Perception Engineer 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
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Own the visual perception pipeline end-to-end, including detection, classification, and tracking of sUAS targets in real time.
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Design and train machine learning models that meet latency and accuracy requirements for edge deployment on Jetson-class hardware.
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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.
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Optimize inference performance on Jetson platforms, including model pruning, quantization, TensorRT integration, and custom kernel development as required.
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Establish the model evaluation framework and metrics used to assess perception performance under operational conditions.
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Deliver target state information (position, velocity, identity, uncertainty) to the controls subsystem.
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Collaborate with the hardware team on camera, optics, and sensor selection.