AI Engineer – Mission Innovation Lab

Carnegie Mellon University

AI Engineer developing real-world AI solutions for warfighting community at Carnegie Mellon. Translating AI concepts into mission-scale capabilities while collaborating with interdisciplinary teams.

Posted 7/23/2026full-timePittsburgh • Pennsylvania, Virginia • 🇺🇸 United StatesSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and developing AI models, implementing end-to-end data pipelines, and creating CI/CD processes for model deployment. Proficient in fine-tuning LLMs and applying advanced machine learning techniques to enhance operational capabilities.

Highest-signal resume keywords

Python ProgrammingC/C++ or Java ProficiencyLangChain FrameworkRetrieval-Augmented Generation (RAG) PipelinesMachine Learning Theory

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

AI Model DevelopmentEnd-to-End Data PipelinesETL ProcessesModel Fine-TuningEvaluation FrameworksRobustness TestingContainerizationREST/GraphQL APIsPEFT/LoRA MethodsModel Context Protocols

Soft Skills

MentoringCollaborationCommunication

Tools & Technologies

DockerKubernetesHugging Face TransformersTensorFlowPyTorchJAXAirflowPrefectRayGitOps

Certifications & Qualifications

Bachelor’s Degree in Computer ScienceMaster’s Degree in Machine Learning or Related FieldDepartment of War Security Clearance

Industry Keywords

AI Safety TechniquesMulti-Step PipelinesModel RegistriesDevSecOps PracticesWarfighting Applications

Tech Stack

Tools & technologies

AirflowDockerETLGraphQLJavaKubernetesPythonPyTorchRayReactTensorflow

About the role

Key responsibilities & impact

De sign, de velop, and fine-tune a variety of AI models
Design autonomous agents and multi-step pipelines using LangChain, ReAct, tool-calling, or custom orchestration
Build Retrieval-Augmented Generation pipelines that combine external knowledge bases with LLMs to improve factual accuracy for warfighting 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

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Machine Learning, Statistics, Applied Mathematics, or a related field with at least eight (8) years of relevant experience, or a MS degree in the same with at least five (5) years of relevant experience
You will be subject to a background investigation and must be able to obtain and maintain an active Department of War (DoW) 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
Proficiency in Python and at least one compiled language (C/C++ or Java)
experience with REST/GraphQL APIs and containerization
Strong grasp of ML theory (supervised, unsupervised, reinforcement learning) and evaluation metrics
Hands-on experience fine-tuning LLMs and using frameworks such as Hugging Face Transformers, LangChain, or comparable agent tools
Familiarity with building RAG pipelines (vector stores, dense/sparse retrievers)
Experience applying PEFT/LoRA methods (e.g., LoRA, adapters) to large models
Understanding of Model Context protocols for managing model state across multi-turn interactions
Experience building evaluation frameworks, benchmarks, or data quality pipelines
Experience with TensorFlow, PyTorch, or JAX; knowledge of data-pipeline tools (Airflow, Prefect, Ray) is a plus.
Awareness of DevSecOps practices (CI/CD, GitOps, container security scanning, model-registry concepts) is desirable

Benefits

Comp & perks

comprehensive medical, prescription, dental, and vision insurance
generous retirement savings program with employer contributions
tuition benefits
ample paid time off and observed holidays
life and accidental death and disability insurance
free Pittsburgh Regional Transit bus pass
access to Family Concierge Team for childcare needs
fitness center access
much more