AI Engineer - Mission Innovation Lab Security Clearance

Position: AI Engineer - Mission Innovation Lab with Security Clearance

At the SEI AI Division, we conduct research in applied artificial intelligence and the engineering questions related to the practical design and implementation of AI technologies and systems. We currently lead a community-wide movement to mature the discipline of AI Engineering for Defense and National Security. As our government customers adopt AI and machine learning to provide leap-ahead mission capabilities, we

• build real-world, mission-scale AI capabilities through solving practical engineering problems
• discover and define the processes, practices, and tools to support operationalizing AI for robust, secure, scalable, and human-centered mission capabilities
• prepare our customers to be ready for the unique challenges of adopting, deploying, using, and maintaining AI capabilities
• identify and investigate emerging AI and AI-adjacent technologies that are rapidly transforming the technology landscape

Are you creative, curious, energetic, collaborative, technology-focused, and hard-working? Are you interested in making a difference by bringing innovation to government organizations and beyond? Apply to join our team.

Overview: As an AI Engineer who thrives at the intersection of deep-learning research and production-grade software development, you will translate cutting-edge AI concepts into robust, mission-scale solutions for the war fighting community.

You will work comfortably with large-scale foundation models such as GPT and LLaMA, designing and deploying agentic workflows, as well as apply and advance traditional ML research and engineering across domains such as natural language processing, computer vision, time series forecasting, and other predictive analytics. You will collaborate closely with senior researchers, software engineers, and government sponsors to define problem statements, iterate on experimental designs, and deliver secure, reliable AI capabilities that meet stringent mission requirements.

The Mission Innovation Lab within the SEI's AI Division works with the defense and national security community to translate the "recently possible" in AI into reliable mission and war fighting capabilities.

Key Responsibilities

• De sign, de velop, and fine - tune a variety of AI model s.
• Design autonomous agents and multi - step pipelines using Lang Chain, ReAct, tool - calling, or custom orchestration; employ the Model Context protocol to manage stateful interactions.
• Build Retrieval - Augmented Generation pipelines that combine external knowledge bases with LLMs to improve factual accuracy for war fighting applications.
• Implement end - to - end data pipelines, ETL processes, and back - end services (Python, C/C++, Java) that feed data to models.
• Create CI/CD pipelines for model training, validation, containerized deployment (Docker/Kubernetes), and security scanning; maintain model registries, monitoring, and version control of context protocols.
• Produce rapid prototypes, run benchmarks, and conduct robustness/adversarial testing in realistic environments.
• Work closely with senior ML engineers, software developers, and government customers; mentor junior staff and contribute to design reviews and documentation.
• Stay current with emerging LLM architectures, agentic paradigms, PEFT/ LoRA methods, and AI - safety techniques; translate new research into operational capabilities.

Required Qualifications

• Bachelor's degree: in Computer Science, Machine Learn ing, Statistics, Applied Mathematics, or a related field with at least four years of relevant experience (or an M.S. with two years).
• Security Clearance: Ability to obtain and maintain an active Department of War ( Do W ) security clearance.
• Onsite Work: You must be able and willing to work onsite 5 days per week at an SEI office in either Pittsburgh, PA or Arlington, VA.
• Programming Proficiency: Proficiency in Python and at least one compiled language (C/C++ or Java); experience with REST/ Graph QL APIs and containerization.
• ML Theory: Strong grasp of ML theory (supervised, unsupervised, reinforcement learning) and evaluation metrics.
• Experience: Hands - on experience fine - tuning LLMs and using frameworks such as Hugging Face Transformers, Lang Chain , or comparable.