SummaryT2S is seeking to hire an
AI/ML Software Engineer and will consist of working with the Research and Development (R&D) team to develop solutions, perform Independent Verification and Validation (IV&V), and creating reports on model and technique effectiveness. We are looking for a candidate with less of a prompt engineering or AI ops focus, and more on the mathematical and data science background.
The ideal candidate will also have experience working in a team environment, is not afraid to dive into internal, vendor or partner solutions to understand how they work, can provide honest and constructive feedback backed up with scientific rigor, and is comfortable explaining to colleagues and leadership the pros and cons of a given solution with integrity.
Responsibilities and Duties - Collaborate with the team on R&D projects throughout their lifecycle, from initial proposal to final wrap up, particularly in how it can/does leverage AI/ML models and products, with a goal of integration into partner DoD systems
- Work with vendors through CRADAs to provide constructive feedback on their products and solutions, to improve accuracy, safety, or meet other requirements of the Branch
- Work with external research partners in academia and industry to guide their research projects to meet the project's goals and collaborate in building a solution that can provide meaningful value to the Branch
- Collaborate and communicate clearly with internal and external development teams, using teams, source control, ticketing, and code review tools
Qualifications and Skills
Required- Must be a U.S. Citizen with an active Secret Clearance (REQUIRED)
- Bachelor's degree in a field related to AI and ML (Computer Science, Artificial Intelligence, Mathematics, Statistics, Data Science, et al) and 3-5+ years of directly related experience
- Proficiency in at least one of the following languages and libraries used within the field (A core competency will be reading and understanding code written by others, and ability to adapt to different styles/architectures is expected): Python (PyTorch, TensorFlow, or Scikit-learn), Rust, or Go
- Proficiency in Git for version control
- Experience in developing or applying one or more of the following types of models or related activities: Classical machine learning paradigms such as computer vision and object classification, Large Language Models (LLMs): Understanding the internal structure and how they function / Prompt engineering, debugging, and validation
- Reinforcement learning techniques, Both Agentic AI (using reprompting) and classical agents
- Diffusion Models for both Image modalities as well as other modalities
- Neuro-symbolic solutions that tie the classical or generative models to verifiable logic
- Adversarial AI/AI Security such as model poisoning, prompt injection, mcp exploits, etc.
- Explainable AI (XAI)
- Experience in deploying models and applications using them in a containerized environment (Kubernetes/Docker)
Desired- TS/SCI Clearance
- Strong proficiency in the mathematical bedrock of AI/ML
- Experience with real-world deployments is a plus
- Experience with air-gapped, secure networks and running models on them
- Knowledge and experience with scaling down models to fit on austere (low resource) platforms
Compensation: 130-160k / annual (DOE)
