Today
Top Secret
Mid Level Career (5+ yrs experience)
$150,000 and above
No Traveling
IT - Data Science
Aberdeen, MD (On-Site/Office)
Peerless is seeking a highly experienced and innovative Senior Research Scientist to lead and support advanced research and development efforts in sensor modeling, simulation, and data exploitation in Aberdeen. The ideal candidate will have deep expertise in AFSIM integration, LIDAR and SAR data processing, and high-performance embedded systems. This role is critical to advancing our capabilities in multi-modal sensor fusion, autonomous systems, and next-generation defense technologies.
Key Responsibilities:
Lead integration of custom performance modeling and AI frameworks into AFSIM for simulation of advanced sensor systems and autonomous platforms.
Design and implement novel algorithms for LIDAR and SAR data exploitation, including low-shot object detection, classification, and 3D reconstruction.
Support the development of synthetic data generation systems using bi-static radar and LIDAR for training and validating ATR algorithms.
Collaborate with cross-functional teams to design and prototype low-power, high-throughput computing platforms for edge AI applications.
Author technical proposals, white papers, and publications; serve as Principal Investigator (PI) on DoD-funded R&D efforts.
Required Qualifications:
Bachelor’s degree in Computer Engineering, Electrical Engineering, or related field.
15+ years of experience in defense-related R&D, with a focus on sensor systems and modeling.
Demonstrated experience with AFSIM, including plugin/module development and simulation integration.
Hands-on experience with LIDAR and SAR data processing, including algorithm development and performance modeling.
Proficiency in C++, Python, CUDA, and embedded systems programming.
Strong background in machine learning, signal processing, and parallel computing.
Proven track record as a Principal Investigator on SBIR/STTR or other government-funded projects.
Preferred Qualifications:
Experience with TechSoft3D HOOPS, Unity, and augmented reality systems.
Familiarity with OpenGL, 3D modeling, and visualization frameworks.
Knowledge of containerization (Docker/Singularity) and deployment of ML models in operational environments.
Publications or patents in relevant fields (sensor fusion, AI, embedded systems).
Key Responsibilities:
Lead integration of custom performance modeling and AI frameworks into AFSIM for simulation of advanced sensor systems and autonomous platforms.
Design and implement novel algorithms for LIDAR and SAR data exploitation, including low-shot object detection, classification, and 3D reconstruction.
Support the development of synthetic data generation systems using bi-static radar and LIDAR for training and validating ATR algorithms.
Collaborate with cross-functional teams to design and prototype low-power, high-throughput computing platforms for edge AI applications.
Author technical proposals, white papers, and publications; serve as Principal Investigator (PI) on DoD-funded R&D efforts.
Required Qualifications:
Bachelor’s degree in Computer Engineering, Electrical Engineering, or related field.
15+ years of experience in defense-related R&D, with a focus on sensor systems and modeling.
Demonstrated experience with AFSIM, including plugin/module development and simulation integration.
Hands-on experience with LIDAR and SAR data processing, including algorithm development and performance modeling.
Proficiency in C++, Python, CUDA, and embedded systems programming.
Strong background in machine learning, signal processing, and parallel computing.
Proven track record as a Principal Investigator on SBIR/STTR or other government-funded projects.
Preferred Qualifications:
Experience with TechSoft3D HOOPS, Unity, and augmented reality systems.
Familiarity with OpenGL, 3D modeling, and visualization frameworks.
Knowledge of containerization (Docker/Singularity) and deployment of ML models in operational environments.
Publications or patents in relevant fields (sensor fusion, AI, embedded systems).
group id: 10120350
