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Allen Control Systems

Computer Vision/Machine Learning Intern, 2027

Posted 2 Months Ago
In-Office
Austin, TX
Internship
In-Office
Austin, TX
Internship
Summer 2027 intern supporting real-time computer vision and machine learning systems for autonomous counter-drone platforms. Responsibilities include detecting, tracking, and classifying aerial threats; generating training data; building data pipelines; optimizing models for NVIDIA Jetson embedded hardware; and experimenting with sensor fusion, tracking robustness, and field performance. The role involves hands-on collaboration with engineers developing deployed defense systems.
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Company Overview
ACS (Allen Control Systems) is a defense technology company building precision robotic systems for the United States and its allies. Founded by two former U.S. Navy electrical engineers with deep experience in robotics and software, ACS brings together AI, computer vision, precision motion, and advanced hardware to solve complex defense challenges across land, air, and maritime environments.

Our flagship product, Bullfrog, is an autonomous precision weapon system that transforms existing weapons into highly accurate counter-drone systems — giving warfighters a scalable, cost-effective response to one of the fastest-growing threats on the modern battlefield. Bullfrog is deployed with U.S. forces, and ACS works with organizations throughout the U.S. military and national security community.

Following a $200 million Series B at a $2.2 billion valuation, ACS is rapidly expanding manufacturing, accelerating Bullfrog deployments, and developing the next generation of autonomous battlefield systems. This is an opportunity to join a proven, fast-moving team and help scale technology with direct, real-world impact on national security.

ACS is headquartered in Austin, Texas, with additional operations in Alexandria, Virginia; Mountain View, California; and Huntsville, Alabama. For more information, visit allencontrolsystems.com.

Our interns aren't fetching coffee or shadowing — they're working alongside the same engineers building the systems we ship. This is early-career work with real stakes.

Location: Austin, TX (on-site)

Duration: Summer 2027 (~10-12 wks)

Pay: Paid internship

Team: Small, hands-on, direct impact

What You Could Work On:

  • Models that detect, track, and classify drones and other aerial threats in real time

  • Training data generation and pipeline work — the unglamorous but critical foundation of any good model

  • Getting models to run fast on real embedded hardware (like NVIDIA Jetson), not just in a notebook

  • Experiments in sensor fusion, tracking robustness, and performance under real-world field conditions

(We're keeping this general on purpose — exact projects depend on where things stand next summer, but this is the flavor of work you'd be doing.)

What We're Looking For:

  • Currently pursuing a degree in Computer Science, Electrical Engineering, Robotics, or a related field

  • Coursework or project experience with PyTorch, TensorFlow, OpenCV, or similar

  • Comfortable in Python, with some exposure to C++ a plus

  • Genuine interest in real-time perception — models that have to work in milliseconds, not just on a benchmark

Why ACS:

  • Direct impact: your work ships on hardware being fielded to protect real people — not stuck in a slide deck.

  • Real ownership: no red tape. You'll work directly with the engineers building Bullfrog, from day one.

  • Small team, big scope: at a startup this size, interns take on real problems, not busywork.

  • Mentorship from engineers who've designed and fielded real defense systems.

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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