Machine Learning Engineer

Position Name:Machine Learning Engineer

Employment Type:Full-time

Notes: Direct Hire. No third-party assistance is approved for this role.

About The Job:

At Jaxon, we’re focused on making AI trustworthy for mission-critical environments. Our core technology is built to enable the safe deployment of AI in high-stakes settings, particularly across the Department of Defense. While our company supports both commercial and government applications, this role is dedicated to the defense side. As a Machine Learning Engineer at Jaxon, you’ll be embedded on a sensitive project designed to help solidify the U.S. government’s ability to use AI reliably.

The work centers on advancing machine learning capabilities to meet the rigor and reliability required for national security operations.

Jaxon’s product, Domain-Specific AI Language (DSAIL), forms the basis of most of our deployments. This role will focus on increasing security and reliability for our customers through implementing and building on DSAIL’s capabilities. A particular emphasis is placed on addressing the fundamental limitations of LLMs — specifically around context and logical reasoning.

The basics of this role requires the ability to:

• Collaborate Across Teams: Work with cross-functional, geographically distributed teams to integrate ML models into our existing systems and workflows, enhancing product capabilities.
• Optimize Data Processing and Model Performance: Conduct comprehensive data management including preprocessing, feature engineering, and model evaluation to improve accuracy and efficiency.
• Technical Proficiency with an NLP focus: Demonstrate solid machine learning engineering experience, particularly with NLP applications and unstructured data in a cloud environment. Fluency in Python is essential.
• Understand Large Language Models: Intimately familiar with the model deployment process, optimization of LLM parameters for specific behaviors, and a general understanding of LLM functionality and use cases. Previous experience with AI/LLM governance and guardrail development is a big plus.

Soft Skills:

• Ability to context switch as needed.
• Ability to communicate findings and recommendations clearly and constructively to peers, management, partners, and customers.
• Ability to effectively explain highly technical details to less technically focused decision makers.
• Ability to compartmentalize different projects with different goals.
• Able to communicate clearly and precisely – not just abstractions and “lines-and-boxes”.

Specific Technologies:

• Python Data Science Stack – Num Py, Pandas, Scikit Learn, various deep learning and NLP libraries including demonstrable experience with libraries relevant to data manipulation.
• Langraph, Llama Index, and/or other agentic libraries.
• Docker, Docker Compose, K8s, and related container orchestration technologies.
• Hands‑on experience running local LLMs using technologies such as Ollama and Transformers.
• Have a comfort level building and training and/or fine‑tuning models, not just calling cloud APIs, but you’re resourceful enough to recognize when you can get away with using cloud APIs.
• Aren’t afraid of digesting academic papers, but you understand that it’s building things that matters.
• Have an understanding of both the fundamentals and applications of different NLP approaches.

Nice-To-Haves:

• Some familiarity with knowledge representation.
• Previous experience with consulting on Defense projects involving software development, data science support, especially at service labs or joint commands.

Developed through R&D with the U.S. Department of Defense, Jaxon converts complex rule sets—whether from policy manuals, onboarding playbooks, compliance protocols, or SME conversations—into executable logic. It extracts the key ‘questions’ hidden in unstructured docs, builds rule‑driven evaluators, and tracks what’s being asked and why. The result: consistent, auditable, and provable reasoning—bringing structure, oversight, and trust to LLM‑driven workflows.

• Human First:
  Our leaders get it — life happens. Whether it’s flexible working hours or…