Staff Computer Vision & Machine Learning Engineer

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 . 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.