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Company Overview
A cutting-edge defense technology startup is developing autonomous systems that combine advanced computer vision, machine learning, and precision control technologies to detect, track, and respond to emerging threats.
With an engineering-first culture, the company values technical excellence, innovation, and the development of technologies with real-world impact. The team is backed by experienced founders with a strong track record in robotics, software, and successful technology ventures.
About the Role
We are looking for a Computer Vision/Machine Learning Engineering Manager to join the Computer Vision and Machine Learning team developing autonomous systems, with a focus on real-time 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 involvement shifting toward management, technical review, and team leadership as the organization scales.
You will partner with senior technical leadership to define the CV/ML technical roadmap and own its execution, translating product goals into team plans, priorities, and delivery commitments as the system progresses from prototype toward a highly reliable, field-ready platform.
What You’ll Do
- Manage and grow a CV/ML team: lead existing engineers, hire across levels from Junior through Senior Staff, and develop engineers' careers and technical capabilities.
- Partner with senior technical leadership to define the CV/ML technical roadmap and own its execution within your team.
- Translate product and company goals, including new system variants, expanded engagement ranges, and system-hardening milestones, into quarterly team plans, priorities, and staffing decisions.
- Contribute directly to the development and optimization of computer vision algorithms for real-time drone detection, tracking, and classification.
- Own delivery commitments for the CV/ML stack and coordinate integration milestones with electrical engineering and hardware teams.
- Drive engineering quality through design and code reviews, rigorous testing, and validation across a wide range of environmental conditions and operational scenarios.
What You’ll Need
- Deep expertise and 10+ years of experience working with machine-learning-based computer vision and traditional image and signal processing, ideally in robotics or autonomous systems, with 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, along with a desire to grow into a broader management role.
- Demonstrated experience owning a technical roadmap or planning process for a perception, autonomy, or similar product area.
- Proficiency in Python and C++, with experience using machine learning frameworks such as TensorFlow, PyTorch, or similar technologies.
- Experience with embedded systems and sensor integration, including cameras, LiDAR, and RADAR, is strongly preferred.
- Comfort driving execution while collaborating with senior technical leadership on technical direction in a fast-moving, engineering-first environment.
You’ll Stand Out
- Formal management experience, including hiring and scaling a CV/ML or perception team at a startup or within a 0-to-1 product environment.
- Ph.D. in computer vision, machine learning, or a related field.
- Experience with object detection and tracking in challenging real-world conditions, including small targets, cluttered backgrounds, or low-contrast imagery.
- Background in defense, aerospace, robotics, or autonomous systems where performance and reliability requirements are critical.
- Experience deploying and optimizing models on edge hardware such as NVIDIA Jetson or similar embedded GPU platforms.
- Experience with multi-sensor fusion across modalities, including optical and infrared cameras.
- Experience leading teams through field testing and hardware integration cycles, rather than software-only development.