Senior AI Engineer

MITRE is hiring a Senior AI Engineer in McLean, VA to support the development, evaluation, and clear communication of results for Artificial Intelligence and Machine Learning solutions. This role involves rapid prototyping and hands-on work across LLMs, NLP, computer vision, data engineering, and MLOps, with opportunities to lead tasks on complex efforts.

Compensation for this onsite position is listed as USD 143,600 to 215,400 per year, with the midpoint noted at $179,500.

Responsibilities

• Rapidly prototype and evaluate AI and ML approaches to determine feasibility and align with project needs.
• Lead simple projects or take accountability for workstreams within complex projects, planning and guiding others as needed.
• In a contributor role, work independently with minimal instruction, guidance, and direction.
• Identify and solve complex problems by applying judgment within the Data Science and Artificial Intelligence disciplines, including taking new perspectives on existing solutions.
• Apply advanced Data Science and AI knowledge and share it with others.
• Communicate complex results in both writing and verbally to technical and non-technical audiences.

Requirements

• Typically requires minimum 5 years of related experience with a Bachelor’s degree, or 3 years with a Master’s degree, or a PhD with relevant experience who can contribute immediately, or an equivalent combination of education and related work experience.
• Hands-on proficiency with AI and Data Science techniques, including Large Language Models, Natural Language Processing, Computer Vision, or other Machine Learning techniques (supervised, unsupervised, deep learning, reinforcement learning).
• Computer programming skills (examples include Python, C/C++, Java, Scala, Julia, and Rust).
• Must have an active Top Secret/SCI with polygraph U.S. Government-issued Security Clearance. U.S. citizenship is required for consideration for a security clearance.
• Requires a minimum of 3 days per week onsite.

Technologies

• Programming and platforms: Python; C/C++, Java, Scala, Julia, Rust; Linux; Git; containers
• AI/ML: Machine Learning, supervised learning, unsupervised learning, deep learning, reinforcement learning
• LLMs and related techniques: Large Language Models, prompt engineering, few-shot learning, Retrieval Augmented Generation (RAG), LLM fine-tuning, LLM hosting, agentic AI frameworks
• Computer Vision and NLP: Object detection, image classification, diffusion models, natural language processing (encoders, summarization, classification, entity recognition, topic modeling)
• Data and distributed processing: PostgreSQL, SQL, NoSQL, vector, graph; Spark, Hadoop, Ray, Dask, Kafka; Airflow, Dagster, NiFi
• MLOps and lifecycle: DevOps for AI, MLOps, model deployment, monitoring, reproducibility

Advanced Data Analytics and Artificial Intelligence

• Machine Learning (supervised, unsupervised, deep learning, reinforcement) and model evaluation
• Large Language Models and techniques (prompt engineering, few-shot learning, RAG, LLM fine-tuning, LLM hosting)
• Agentic AI frameworks and approaches
• DevOps for AI
• Computer Vision (object detection, image classification, diffusion models)
• Natural Language Processing (encoders, summarization, classification, entity recognition, topic modeling)
• Rapid prototype development
• Computer programming required in Python; preferred in C/C++, Java, Scala, Julia, Rust
• Databases: SQL, NoSQL, vector, graph

Data Engineering

• Distributed data processing and big data architectures (Spark, Hadoop, Ray, Dask, Kafka)
• Data pipeline/ETL development and orchestration (Airflow, Dagster, NiFi)
• Batch and streaming data processing
• Data modeling, schema design, and data quality assessment
• Data governance, access controls, and secure data handling
• Cloud-based data platforms and storage architectures

Software Development and MLOps

• Computer programming and databases: Python and SQL proficiency required
• Software engineering best practices (version control, testing, modular design)
• MLOps and model operationalization (model deployment, monitoring, reproducibility, containerization)
• Linux-based development environments, Git, containers, and cloud platforms

Clearance Requirements

• Requisition requires the candidate to have a minimum clearance of Top Secret/SCI/Polygraph.
• Requisition requires the hired candidate to have or obtain within one year from date of hire the clearance Top Secret/SCI/Polygraph.

Preferred qualifications: Master’s degree in Computer Science, Mathematics, Applied Mathematics, Data Science, or related field with 3 years of relevant experience; or a PhD in Computer Science, Mathematics, Applied Mathematics, Data Science, or related field.