AI Research Scientist

Pacific Defense•Fairfax, VA

13h

About The Position

Perceptronics is seeking an AI Research Scientist to join our research and development team. We are looking for candidates who enjoy solving difficult, often poorly defined technical problems and developing new algorithmic solutions. We are interested in candidates from a range of technical backgrounds, including artificial intelligence, machine learning, signal processing, controls, operations research, applied mathematics, statistics, robotics, and related fields. The common thread is an ability to formulate technical problems, identify promising approaches, develop and evaluate algorithms, and improve those approaches based on experimental results and a deeper understanding of the problem. Our work spans a broad range of methods, including statistical inference and estimation, clustering and density estimation, optimization, search and planning, tracking and filtering, signal processing, sensor fusion, machine learning, and related techniques. Machine learning is an important part of our work, but the appropriate solution to a problem may involve traditional algorithms, statistical methods, machine learning, or a combination of approaches. We are particularly interested in candidates who can critically evaluate alternative approaches, understand their assumptions and limitations, and adapt or extend state-of-the-art academic research to solve real-world DoD problems. Our application areas include electronic warfare, RF signal analysis, autonomy, unmanned systems, multi-agent systems, sensor systems, and other national security problems. Prior experience in one or more of these domains is highly valuable but is not required for candidates with strong algorithmic and research capabilities.

Requirements

Bachelor’s, Master’s, or PhD in Computer Science, Artificial Intelligence, Electrical Engineering, Robotics, Applied Mathematics, Statistics, Operations Research, Controls, or a related technical field. An advanced degree is preferred.
Strong foundation in algorithms and one or more areas such as probability and statistics, optimization, estimation, signal processing, control theory, search and planning, or machine learning.
Experience developing algorithms in one or more areas such as statistical estimation, optimization, search and planning, controls, signal processing, tracking, sensor fusion, clustering, anomaly detection, or machine learning. Breadth across multiple areas is a strong plus.
Ability to read and understand technical literature and translate research ideas into working algorithms.
Strong critical-thinking and problem-solving skills, including the ability to reason about assumptions, tradeoffs, failure modes, and alternative approaches.
Strong programming skills in Python, C++, or similar languages.
Experience with electronic warfare, RF signal processing, autonomy, robotics, unmanned systems, multi-agent systems, tracking, sensing, or related defense applications is highly desirable but not required.
Excellent communication skills and the ability to work effectively as part of a collaborative technical team.
U.S. citizenship and willing to obtain a security clearance required.

Nice To Haves

Experience with scientific computing and machine learning libraries is beneficial, but we place greater emphasis on understanding and developing algorithms than on experience with any particular framework.

Responsibilities

Investigate challenging technical problems and formulate algorithmic approaches for solving them.
Perform literature reviews to identify promising methods, understand their strengths and limitations, and determine how they can be adapted to problems of interest.
Design, implement, test, and improve prototype algorithms using techniques drawn from AI, statistics, optimization, estimation, search, signal processing, machine learning, and related fields.
Analyze complex data and system behavior to understand underlying structure, identify meaningful patterns, and select appropriate computational approaches.
Compare alternative approaches and refine algorithms based on experimental results, theoretical insight, and operational requirements.
Collaborate with scientists and engineers to integrate algorithms into operational software and hardware systems.
Communicate technical approaches, experimental results, and recommendations to both technical and non-technical stakeholders.
Stay current with relevant academic research and emerging methods and identify opportunities to apply them to Perceptronics problems.