Director of Computer Vision and Machine Learning

About The Role

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.

Qualifications

• Bachelor's degree in Computer Science, Electrical Engineering, Robotics, or a related field.
• Experience with real-time or embedded perception systems, including model optimization and edge deployment; familiarity with tracking algorithms such as Kalman filters and data association methods.
• Familiarity with MLOps practices, large-scale training pipelines, and data engineering; systems engineering experience including requirements decomposition, interface definition, and verification.

Responsibilities

• Translate the VP / Chief Engineer of AI's vision and technical strategy into actionable plans, milestones, and priorities for the CV, ML, and Tracking teams; serve as the primary communication channel carrying direction down and status, risk, and insights up.
• Manage, coach, and develop the managers of the CV/ML and Tracking teams, holding them accountable for delivery, performance, and team health; mentor engineers and technical leads and help identify and grow future leaders.
• Partner with software, mechanical, electrical, and systems engineering to solve problems no single discipline can solve alone, including sensor selection and placement, optics, compute and power constraints, and thermal limits.
• Own on-time delivery of CV/ML and Tracking contributions to program schedules; anticipate integration risks early and drive resolution before they threaten milestones, escalating when schedule risk cannot be resolved at the working level.
• Establish and enforce standards for model development, evaluation, experiment tracking, reproducibility, and documentation; define metrics and benchmarks such as detection accuracy, tracking performance, latency, and robustness so success is measurable.
• Review code and pull requests, and write code, prototypes, or analysis scripts to unblock the team or validate an approach; participate in architecture and design reviews, challenging assumptions and guiding teams toward robust, scalable solutions.
• 10-15 years of experience in computer vision and/or machine learning, with 3+ years leading managers or multiple teams.
• Bachelor's degree in Computer Science, Electrical Engineering, Robotics, or a related field.
• Deep expertise in at least one of the following: object detection, multi-object tracking, sensor fusion, or deep learning for perception.
• Strong programming skills in Python, with familiarity in C++ and common frameworks including PyTorch, TensorRT, and OpenCV; proven track record taking CV/ML systems from research through production deployment.
• Demonstrated success collaborating across software, mechanical, electrical, and systems disciplines to deliver complex products on schedule; excellent communication skills engaging with staff engineers, peers, and C-level executives; comfort operating in a flat, collaborative, low-ego, high-ownership organization.
• Master's or PhD in Computer Science, Electrical Engineering, Robotics, or a related field.
• 15-20 years of experience leading managers across multiple teams, with experience scaling computer vision/ML organizations beyond a single team.
• Experience with real-time or embedded perception systems, including model optimization and edge deployment; familiarity with tracking algorithms such as Kalman filters and data association methods.
• Familiarity with MLOps practices, large-scale training pipelines, and data engineering; systems engineering experience including requirements decomposition, interface definition, and verification.
• Experience in a domain such as autonomous systems, robotics, defense, surveillance, or sports analytics; publications, patents, or open-source contributions in the field.