CV/ML Engineering Manager

Allen Control Systems•Austin, TX

About The Position

Computer Vision & Machine Learning Engineering Manager for Autonomous Anti-Drone Systems 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 venture acquired for a combined $180M in 2022, we are committed to ensuring that the groundbreaking technologies we develop will have a real-world impact. About The Role We are looking for a Computer Vision/Machine Learning Engineering Manager to join the Computer Vision and Machine Learning team behind our autonomous gun turret, with a focus on real-time drone detection, tracking, and classification. This begins as a player-coach role: you will contribute directly to the CV/ML stack while managing and growing the team, with your hands-on share shifting toward management and technical review as the team scales. You will partner with our Chief Engineers and VP of AI to define the CV/ML technical roadmap and own its execution, translating product goals into team plans and delivery commitments as we harden the prototype into a military-grade system.

Requirements

Deep expertise and 10+ years of experience working on machine-learning-based computer vision and traditional image and signal processing, ideally in the context of robotics, including a proven track record of deploying CV systems in real-time or safety-critical applications.
At least a Bachelor's degree in Computer Science, Electrical Engineering, or a related field, with a strong focus on machine learning and computer vision.
Experience managing or tech-leading a team of CV/ML engineers, and a desire to grow into a larger management scope.
Evidence of successfully owning a technical roadmap or planning process for a perception, autonomy, or similar product area.
Proficiency in Python and C++, and experience with machine learning frameworks such as TensorFlow, PyTorch, or similar; experience with embedded systems and sensor integration (cameras, LIDAR, RADAR) strongly preferred.
Comfort driving execution while collaborating on technical direction with senior technical leadership, in a fast-moving, engineering-first environment.

Nice To Haves

Formal management experience, including having hired and scaled a CV/ML or perception team at a startup or in a 0-to-1 product environment.
Ph.D. in computer vision, machine learning, or related fields.
Experience with object detection and tracking in challenging real-world conditions such as small targets, cluttered backgrounds, or low-contrast imagery.
Background in defense, aerospace, or autonomous systems where performance and reliability requirements are non-negotiable.
Experience deploying and optimizing models on edge hardware such as NVIDIA Jetson or similar embedded GPU platforms, or with multi-sensor fusion across modalities such as optical and infrared cameras.
Experience leading teams through field testing and hardware integration cycles, not just software-only development.

Responsibilities

Manage and grow a CV/ML team: lead the existing engineers, hire across levels from Junior through Senior Staff, and develop your engineers' careers and technical skills.
Partner with Chief Engineers and VP of AI to define the CV/ML technical roadmap, and own its execution within your team.
Translate product and company goals, such as new weapon system variants, engagement ranges, hardening milestones, into quarterly team plans, priorities, and staffing decisions.
Contribute directly to development and optimization of computer vision algorithms for real-time drone detection, tracking, and classification.
Own delivery commitments for the CV/ML stack, coordinating integration milestones with electrical engineering and hardware teams.
Drive engineering quality through design and code review, and rigorous testing and validation practices across environmental conditions and scenarios.