AI Research Scientist, Applied AI

Must have an Active US Security Clearance (Secret Minimum, Top Secret Preferred) US Security Clearances

Masters in related field + Minimum of 2 years' experience in similar role (or PhD with relevant research projects)

AI/ML expertise Demonstrable experience in the application of AI/ML methodologies including, but not limited to, deep learning, generative models (e.g. LLMs, diffusion models), agentic systems, reinforcement learning, computer vision, or other emerging areas of AI research

Demonstrable experience in the application of AI/ML methodologies including, but not limited to, deep learning, generative models (e.g. LLMs, diffusion models), agentic systems, reinforcement learning, computer vision, or other emerging areas of AI research

Software development experience Familiarity with object-oriented paradigms and functional programming principles Expertise in at least one modern high-level programming language (e.g. Python, R, C++, Java) Collaborative source code management and maintenance processes (e.g. Github, code reviews, CI/CD)

Familiarity with object-oriented paradigms and functional programming principles

Expertise in at least one modern high-level programming language (e.g. Python, R, C++, Java)

Collaborative source code management and maintenance processes (e.g. Github, code reviews, CI/CD)

Ability to work within multi-disciplinary teams in a fast-paced, evolving operational environment that spans military, government, and industry partners

Excellent verbal and written communication skills

Passion for Space and AI/ML applications

Experience with fine-tuning LLMs, prompt engineering, retrieval-augmented generation (RAG), and domain adaptation for scientific/engineering datasets using modern ML frameworks and model hubs

Familiarity with Reinforcement Learning (RL) and multi-agent reinforcement learning to enable training agents that learn strategies in simulation and real-world contexts

Practical understanding of neural networks, transformer architectures, attention mechanisms, and optimization methods to extend or fine-tune transformer-based models

Experience building reusable internal tools (connectors, simulation frameworks, evaluation harnesses) to refine simulation-based datasets for AI agent training in support of research, data-driven insights/analytics, and model development

Hands-on experience supporting the development and deployment of supervised and/or unsupervised learning models

One or more peer reviewed articles, conference papers, and/or presentations in a science or engineering discipline

Demonstrable combined experience indicative of skillsets required to utilize APIs, microservices, and workflows that merge physics simulation engines with AI training pipelines

Familiarity with common agentic protocols (MCP, A2A, etc...)

Practical working experience with physics-based simulation, and statistical methods (e.g. monte Carlo methods, probabilistic modeling, and Bayesian methods)

Working knowledge of parallel computing, GPU acceleration, and performance optimization for simulations and training workloads.

Experience with space and astrodynamics is valuable but not required

We're building a constellation here, not looking for identical satellites. Every member of the team brings different capabilities to the same mission. If your orbit intersects with ours and you're mission-ready, send it.