Machine Perception (Vision) Engineer

About Quartermaster

At Quartermaster AI, we believe the ocean should be a safe and sustainably managed resource for all. By leveraging cutting-edge AI and robotics, we unlock capabilities that were only recently impossible. Our distributed open-ocean systems enable every vessel to sense, compute, and communicate, enhancing maritime domain awareness for those who need it most.

Equal Employment Opportunity (EEO) Statement

Quartermaster AI is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, genetic information, or any other protected status under applicable federal, state, or local laws.

We encourage individuals of all backgrounds to apply and join us in shaping the future of defense technology. If you require accommodations during the application or interview process, please let us know.

About the Role:

We are seeking a highly experienced Machine Perception Engineer to lead the development of edge-deployed perception and intelligence systems for maritime environments. This role is core to our mission: enabling vessels to autonomously understand their surroundings through real-time scene analysis, object detection, classification, and tracking. You’ll design and optimize vision models that run efficiently on-device under constrained compute conditions. Your work will involve fusing data from multiple sensors (camera, radar, RF) to enable robust vessel identification, safety features, and anomaly detection in challenging field conditions. The ideal candidate has extensive hands-on experience deploying vision systems in production, especially in edge or low-bandwidth environments, and is comfortable translating applied research into performant, maintainable code.

Key Responsibilities:

• Lead the design, training, and deployment of computer vision and perception models for tasks including vessel detection, classification, and tracking.
• Architect and optimize inference pipelines for real-time performance on edge devices, including GPU-accelerated and mixed-precision inference.
• Implement and refine algorithms for scene understanding, object segmentation, and environmental awareness using both image and sensor fusion data.
• Collaborate with hardware and software teams to ensure model compatibility with embedded systems and available compute resources. Develop techniques for edge-case detection, dataset curation, and self-healing model behavior in dynamic and non-ideal environments.
• Apply active learning, domain adaptation, and synthetic data strategies to improve model robustness and generalization.
• Work closely with RF and sensor teams to integrate complementary signal data into multimodal classification and detection pipelines.
• Lead model evaluation processes and benchmarking across hardware platforms and field conditions.
• Maintain best practices for model versioning, traceability, and validation in safety-critical applications.

Qualifications (Preferred):

• Bachelor's, Master’s or Ph.D. in Computer Vision, Machine Learning, Robotics, or related field.
• 8+ years of experience in applied perception or computer vision, in performance critical applications Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
• Experience with model optimization techniques including pruning, quantization, or TensorRT deployment.
• Strong understanding of classical vision (OpenCV) as well as modern deep learning methods.
• Familiarity with maritime, aerospace, or other harsh-environment sensing applications is a strong plus.
• Experience with sensor fusion, Kalman filtering, or multi-modal neural networks.
• Excellent problem-solving, debugging, and software design skills.
• Strong communication skills, with the ability to lead technical direction and mentor junior engineers.

Work Environment:

• This is a remote position with collaboration via online tools.
• Flexible working hours with occasional deadlines requiring high availability.
• Opportunity to work on innovative projects with a global impact.

Benefits:

• Competitive salary.
• Flexible work hours and the option for remote work.
• Opportunities for professional development and continued education.

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