About the job
As a programmer, you know that machine learning is critical to understanding and processing massive datasets. Your ability to conduct statistical analyses on business processes using ML techniques makes you an integral part of delivering a customer-focused solution. As a machine learning engineer on our autonomy team, you’ll train, test, deploy, and maintain models that learn from data. In this role, you’ll contribute to the direction of mission-critical solutions by applying best-fit ML algorithms and technologies. You will be responsible for the end-to-end training and deployment of edge LLMs and SLM, starting from data gathering or generation to training via LoRA, SFT, and unsupervised training like RL. Work with us to solve real-world challenges and define ML strategy for the U.S. military and operators.
Responsibilities
train, test, deploy, and maintain models that learn from data; contribute to the direction of mission-critical solutions by applying best-fit ML algorithms and technologies; be responsible for the end-to-end training and deployment of edge LLMs and SLM, starting from data gathering or generation to training via LoRA, SFT, and unsupervised training like RL; work with us to solve real-world challenges and define ML strategy for the U.S. military and operators
Qualifications
Minimum
4+ years of experience with machine learning and AI, including training, testing, and deploying models; 3+ years of experience with GenAI, including fine-tuning; Experience performing distributed training across large GPU clusters; Experience with LangChain, LangGraph, AutoGen, or LlamaIndex; Experience with Model Context Protocol (MCP) for tool integration and A2A for agent-to-agent collaboration; Experience in RAG architecture; Knowledge of graphs, including Neo4j or NebulaGraph; Knowledge of modern software design patterns, including microservice design or edge computing; Ability to obtain a Secret clearance; Bachelor’s degree in a Computer Science or AI field
Preferred
Experience generating datasets for LLM or SLM fine-tuning; Experience deploying models to compute-constrained environments; Master’s degree in a Computer Science, AI, or similar field preferred; Doctorate degree in Computer Science, Statistics, or a similar field a plus