CV/ML Engineer (Tracking Specialist)

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 ventures acquired for a combined $180M in 2022, we are committed to ensuring that the groundbreaking technologies we develop have a real-world impact.

Position Overview

We are seeking a Computer Vision / Machine Learning Engineer (Tracking Specialist) to join our team. This person will join our CV/ML team and specialize in object detection and tracking for robotics and autonomous systems. In this role, you will design and implement real-time perception algorithms that enable robots and autonomous platforms to detect, track, and understand objects in dynamic environments. You will work closely with robotics, software, and platform engineers to develop high-performance computer vision systems that operate reliably in real-world conditions and on resource-constrained hardware. This role is ideal for engineers who enjoy solving challenging perception problems, building real-time systems, and working at the intersection of machine learning, robotics, and computer vision.

What You'll Do

• Develop and optimize computer vision algorithms for real-time detection and tracking of small objects in robotic and autonomous systems.
• Implement and improve object detection and multi-object tracking pipelines using modern deep learning techniques.
• Train, evaluate, and deploy machine learning models for perception tasks in dynamic environments.
• Design image preprocessing and feature extraction pipelines to improve model robustness and performance.
• Optimize models and algorithms for low-latency inference on GPU or embedded hardware.
• Collaborate with robotics and software teams to integrate perception systems into autonomous platforms and real-world deployments.
• Analyze system performance and improve tracking accuracy, stability, and reliability in challenging environments.
• Support the development of datasets, evaluation metrics, and testing frameworks for perception systems.
  
  

Required Technical Skills

• 2-5 years of experience with CV/ML Engineering experience with robotics and comfortable in a role that specializes in tracking
• Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field
• Strong experience developing computer vision algorithms for real-time applications
• Experience implementing object detection (YOLO, SSD, R-CNN, or similar) and classic or deep learning-based tracking algorithms (Kalman and particle filters, ByteTrack, or similar)
• Experience with machine learning frameworks such as PyTorch, TensorFlow, or similar
• Experience with image preprocessing, feature extraction, and other classical computer vision pipelines
• Strong programming skills in Python and/or C
  
  

Preferred Technical Skills

• Production experience integrating real-time tracking algorithms into robotic perception systems on edge or embedded hardware.
• Experience integrating sensors into autonomous systems, including cameras, LiDAR, or RADAR
• Experience working on robotics systems or embedded vision platforms
• Experience optimizing vision workloads using CUDA or GPU acceleration
  
  

What We Offer

• Competitive salary
• 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.