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Salary
$203k – $270k per year
Location
In office (Austin, Mountain View)
Seniority
Staff · 10+ years exp
Employment
Full-Time
Overview
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Impact
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Allen Control Systems is a defense technology company specializing in the development of autonomous precision weapon stations and counter-drone solutions for modern battlefields. The organization utilizes advanced robotics, computer vision, and artificial intelligence to automatically detect, track, and neutralize unmanned aerial threats.

Staff Computer Vision & Machine Learning Engineer for Autonomous Anti-Drone Systems

Company Overview:

Allen Control Systems (ACS) is a cutting-edge defense startup founded by two former Navy electrical engineers with a proven track record in robotics and software. We are developing an autonomous gun turret using advanced computer vision and control systems to precisely detect, track, and neutralize enemy drones.

With an engineering-first culture, ACS values technical excellence and innovation. Backed by our founders' successful exits from two previous venture acquired for a combined $180M in 2022, we are committed to ensuring that the groundbreaking technologies we develop will have a real-world impact.

About The Role:

We are looking for a Staff Level Computer Vision and Machine Learning Engineer to help lead the development and optimization of the CV/ML systems at the core of our autonomous gun turret, with a focus on real-time drone detection, tracking, and classification. You will work closely with electrical engineers to integrate vision systems into the turret’s hardware architecture and contribute to hardening the prototype into a military-grade system.

What You'll Do:

  • Development and optimization of computer vision algorithms for our autonomous gun turret, focusing on real-time drone detection, tracking, and classification.

  • Design and implement machine learning models that can operate in resource-constrained environments while maintaining high accuracy and reliability.

  • Collaborate closely with electrical engineers to integrate computer vision systems into the turret's hardware architecture.

  • Conduct extensive testing and validation of computer vision algorithms in various scenarios to ensure robustness and performance under different environmental conditions.

  • Contribute to the hardening of the prototype turret into a military-grade system, and assist in developing variants for different weapon systems and engagement ranges.

What You'll Need:

  • Deep passion for machine learning, computer vision, and robotics, and have been exploring these areas since early in your career.

  • More than 10+ years of experience working on machine-learning-based computer vision, ideally in the context of robotics.

  • At least a Bachelors and/or Masters Degree in Computer Science, Electrical Engineering, or a related field, with a strong focus on machine learning and computer vision.

  • A proven track record of senior-level experience developing and deploying computer vision systems, ideally in real-time or safety-critical applications.

  • Proficient in Python, C++, and have experience with machine learning frameworks such as TensorFlow, PyTorch, or similar.

  • Experience with embedded systems and integrating computer vision algorithms into hardware.

  • Familiar with various sensors (e.g., cameras, LIDAR, RADAR) and their integration into autonomous systems.

  • You enjoy collaborating with other engineers to solve complex technical challenges.

What We Offer:

  • Competitive salary

  • ACS Equity Package

  • Health, Dental, Vision Insurance

  • Paid Time Off

Allen Control Systems is an Equal Opportunity Employer, providing equal employment opportunities to all employees and applicants for employment. Allen Control Systems prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

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