AI Systems Engineer – Multi Modal
Quartermaster AI
full-time
Posted on:
Location Type: Remote
Location: Remote • 🇺🇸 United States
Visit company websiteJob Level
SeniorLead
Tech Stack
PythonPyTorchTensorflow
About the role
- Research, design, and implement advanced machine learning models that combine vision, RF, and acoustic signals for detection, classification, and tracking tasks
- Architect sensor fusion pipelines that support robust, redundant, and context-aware perception in dynamic environments
- Collaborate closely with domain experts and systems engineers to translate raw sensor data into actionable model inputs
- Design and oversee data pipelines for multi-modal learning, including data alignment, augmentation, and pre-processing across modalities
- Optimize models and inference workflows for low-latency execution on embedded and edge compute platforms
- Lead performance analysis across individual and fused modalities, and drive strategies for improving robustness and generalization
- Prototype and operationalize novel research in sensor fusion, uncertainty modeling, and representation learning
- Contribute to long-term architectural decisions around multi-modal AI infrastructure, tooling, and evaluation frameworks
- Document model design, training methodology, and validation processes with rigor and clarity.
Requirements
- PhD or Master’s degree in Machine Learning, Computer Vision, Signal Processing, or a closely related field
- 7+ years of experience building and deploying machine learning systems, with a focus on multi-modal or sensor fusion applications
- Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow
- Demonstrated experience working with camera imagery, RF signals and/or acoustic data
- Deep understanding of signal alignment, temporal/spatial synchronization, and feature extraction across diverse data types
- Proven ability to bridge research and application—delivering high-performance models in production contexts
- Excellent communication and collaboration skills in cross-functional, interdisciplinary teams
- Experience in maritime, aerospace, or other sensor-rich environments is a significant plus.
Benefits
- Competitive salary
- Flexible work hours and the option for remote work.
- Opportunities for professional development and continued education.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
machine learningsensor fusiondata pipelinesmulti-modal learningmodel optimizationinference workflowsfeature extractionsignal processingPythondeep learning frameworks
Soft skills
communicationcollaborationleadershipinterdisciplinary teamworkperformance analysisproblem-solvingdocumentationrigorclaritystrategic thinking
Certifications
PhD in Machine LearningMaster’s degree in Machine Learning