Embedded CV/ML Intern — Real Impact, Equity Eligible

Allen Control Systems; Austin (TX); On-site; USD 36,000 - 60,000; Full time

Benefits offered by this job

Competitive pay

ACS Equity Package

Health, Dental, Vision Insurance

Paid Time Off

Job summary

Allen Control Systems is seeking a back-end internship in Austin, TX for Summer 2027. You will work directly with engineers building Bullfrog and related autonomous systems, gaining real-world experience on high-impact projects.

This on-site internship emphasizes hands-on development, with proximity to the same teams that ship to the U.S. military. Expect ~10–12 weeks of collaborative, fast-paced work with mentorship and practical outcomes.

Qualifications

• 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

Responsibilities

• Work on models that detect, track, and classify drones and other aerial threats in real time.
• Contribute to training data generation and pipeline work for model development.
• Get models to run fast on embedded hardware (e.g., NVIDIA Jetson).
• Experiment with sensor fusion, tracking robustness, and performance under real-world conditions.

Skills

Python

C++

PyTorch

TensorFlow

OpenCV

Real-time perception

Education

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

Tools

PyTorch

TensorFlow

OpenCV

Job description

Allen Control Systems is seeking a back-end internship in Austin, TX for Summer 2027. You will work directly with engineers building Bullfrog and related autonomous systems, gaining real-world experience on high-impact projects.

This on-site internship emphasizes hands-on development, with proximity to the same teams that ship to the U.S. military. Expect ~10–12 weeks of collaborative, fast-paced work with mentorship and practical outcomes.