AI/ML Engineer (Natural Language Processing & Large Language Models)

Quick Facts

• Role: AI/ML Engineer focused on NLP and advanced language models

• Clearance: Requires in-scope TS/SCI with polygraph clearance

Description

Build, train, fine-tune, evaluate, and help deploy advanced language models to address business and customer requirements. You will work across the full model development lifecycle—covering data/model workflows, evaluation, integration, and production performance improvement—while collaborating with cross-functional teams. Limited telework may be available after onboarding and required training.

Responsibilities

• Build, train, and fine-tune machine learning models, particularly language models

• Apply best practices for model training, tuning, and optimization

• Select datasets and perform statistical analysis

• Run rigorous model evaluation, benchmarking, and error analysis/diagnostics

• Improve model performance through iterative development and feedback

• Deploy and scale models using cloud AI platforms, especially AWS SageMaker

• Integrate ML models into production environments and provide system integration oversight

• Contribute to full-stack development for AI model-based applications (front-end and back-end)

• Collaborate with data scientists, product teams, and engineers to translate requirements

• Document workflows, model design processes, and technical specifications

Requirements

• In-scope TS/SCI with poly clearance (within 7 years)

• 2+ years of applied machine learning experience on programs/contracts of similar scope, type, and complexity

• B.S. in advanced math (calculus, linear algebra, Bayesian statistics), computer science, or related STEM (or 3 additional years of ML experience substituting for the degree)

• Knowledge and experience with language models and natural language processing (NLP)

• Experience with Marian and multilingual model development

• Strong programming experience in Java and C/C++

• Hands-on experience with ML frameworks/libraries and standard test/evaluation protocols

• Hands-on experience deploying models on AWS (SageMaker; also Lambda/EC2/S3)

• Solid understanding of data structures, algorithms, and system design

Benefits

• Generous paid time off (based on time in service) plus 11 paid annual holidays

• Employer-paid medical (base plan), dental, vision, disability, life, EAP, and more

• SEP-IRA contribution: 10% of monthly gross salary (employee-owned; non-contributory; no vesting period)

• Educational assistance up to $5,250 annually (upon prior approval)

• Training assistance up to $3,000 annually (upon prior approval)