Research Scientist

About The Opportunity

This job description is intended to describe the general nature and level of work being performed by people assigned to this classification. It is not intended to be construed as an exhaustive list of all responsibilities, duties and skills required of personnel so classified.

Responsibilities

• Provide technical contributions as a software engineer for a wide range of projects involving machine learning (ML) and artificial intelligence (AI), including autonomy, sensing and communication, and decision support systems, among others.
• Work collaboratively with multi-disciplinary teams across the KRI consortium, consisting of academic and industry partners, to create solutions and prototypes for projects in application areas, including autonomous systems, robotics, cognitive and distributed sensing, and machine learning systems, among others.
• Contribute to the design, implementation, testing, or evaluation of ML/AI-enabled or simulation-driven software systems.
• Hands-on experience with machine learning frameworks (e.g., PyTorch), including model training, evaluation, and experimentation.
• Familiarity with distributed or accelerated computing environments (e.g., GPU-enabled systems, shared compute clusters).
• Experience contributing to the design, implementation, testing, or evaluation of ML/AI-enabled or simulation-driven software systems.
• Experience or coursework involving modeling and simulation techniques, such as: Network, agent-based, or discrete-event simulation, Monte Carlo or stochastic simulation methods, Synthetic data generation or simulation-in-the-loop workflows.
• Experience working with multidisciplinary teams across research, engineering, and applied R&D environments.

Requirements

• Required Education & Experience: Bachelor’s or Master’s degree in Electrical Engineering, Computer Engineering, Computer Science, Applied Mathematics, or a closely related field. 2–4 years of professional experience in software engineering, data science, or applied R&D, with exposure to machine learning and AI system development in research, prototype, or production environments.
• Preferred Master’s degree with a focus on ML/AI, data-intensive systems, network science, optimization, or related areas.
• Experience contributing to government, defense, or security-related R&D programs (internships, fellowships, or full-time roles).
• Familiarity with simulation-based models (e.g., physics-based, network-based, agent-based, or stochastic simulations) for analysis, experimentation, or decision support.

Skills & Attributes

• Proficiency in Python and familiarity with modern ML/AI development workflows.
• Experience contributing to the design, implementation, testing, or evaluation of ML/AI-enabled or simulation-driven software systems.
• Experience or coursework involving modeling and simulation techniques, such as: Network, agent-based, or discrete-event simulation, Monte Carlo or stochastic simulation methods, Synthetic data generation or simulation-in-the-loop workflows.
• Experience working with multidisciplinary teams across research, engineering, and applied R&D environments.

Qualifications

• A U.S. Citizenship with the ability to obtain and maintain a security clearance.

Desired Skills & Attributes

• Exposure to Retrieval-Augmented Generation (RAG), vector databases, embedding pipelines, or LLM-enabled systems.
• Familiarity with network science or graph analytics concepts, including: Graph modeling and analysis using tools such as NetworkX, Introductory experience with graph-based ML or GNNs is a plus.
• Experience or coursework involving modeling and simulation techniques, such as: Network, agent-based, or discrete-event simulation, Monte Carlo or stochastic simulation methods, Synthetic data generation or simulation-in-the-loop workflows.
• Experience working with multidisciplinary teams across research, engineering, and applied R&D environments.

Key Responsibilities & Accountabilities

• Software R&D activities, including software development and implementation, prototype modeling & simulation, design, and experimentation. (45%)
• Test and validation of software systems and software for prototype deployment. (45%)
• Provide software development subject matter expertise across a diverse set of application areas and contribute to proposals, publications, whitepapers, etc. (10%)