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 a Machine Learning Engineer who can take research ideas from concept to prototype, you will lead independent applied‑research projects for defense‑focused missions. 

The ideal candidate is comfortable across the full stack (data pipelines, model development, API services, and secure deployment) and eager to explore novel AI and ML theory while delivering mission‑scale capabilities. 

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 warfighting capabilities.  

Key Responsibilities  

• Design, implement, and evaluate state‑of‑the‑art ML models (computer‑vision, NLP, planning, etc.) using frameworks such as TensorFlow, PyTorch, Torch, or Caffe.  

• Build and maintain robust data pipelines, ETL processes, and backend services in Python, C/C++, and Java.  

• Lead rapid‑prototyping efforts, translate research results into operational prototypes, and test for performance, robustness, and security.  

• Define and refine DevSecOps practices for ML (model registries, containerized deployment, continuous integration/continuous delivery, security scanning).  

• Mentor junior team members, collaborate with researchers, government customers, and other engineers, and contribute to technical strategy for the lab. 

Required Qualifications  

• B.S. in Computer Science, Electrical Engineering, Statistics, or related field with ≥10 years of experience ; OR M.S. with ≥8 years ; OR Ph.D. with ≥5 years of relevant experience. 

• Ability to obtain and maintain an active Department of War security clearance. 
   

• You must be able and willing to work onsite 5 days per week at an SEI office in either Pittsburgh, PA or Arlington, VA.

• Strong experience in one or more programming language such as Python, C/C++, and Java; comfortable developing production‑grade code and APIs. 

• Solid understanding of ML theory, statistical learning, and common algorithms. 

• Hands‑on experience with TensorFlow, PyTorch, Torch, Caffe, or similar deep‑learning libraries. 

• Familiarity with CI/CD pipelines, container orchestration (Docker/Kubernetes), model versioning, and security‑focused tooling. 

Desired Experience  

• Proven track record of independent applied‑research projects that resulted in demonstrable prototypes or operational capabilities. 

• Publications or open‑source contributions in AI and ML, especially in adversarial or robust ML. 

• Experience working on defense or other high‑impact government programs.  

• Ability to quickly learn emerging AI and ML technologies and translate them into mission‑relevant solutions. 

Knowledge, Skills, & Abilities  

• Deep technical knowledge of modern ML methods and ability to extend them to novel domains. 

• Excellent written and verbal communication skills; capable of presenting complex ideas to technical and non‑technical audiences. 

• Strong collaborative mindset; experience working in interdisciplinary teams and mentoring peers. 

• High degree of scientific curiosity and a proactive, self‑directed work style.