Job description

ACTIVE SECURITY CLEARANCE AT THE TS/SCI POLYGRAPH LEVEL IS REQUIRED We are seeking a visionary, inventive Artificial Intelligence / Machine Learning (AI/ML) Engineer to design, build, test, and productize advanced models that solve high-stakes challenges. In this role, you won't just train models in isolation—you will transform data science prototypes into production-ready, scalable AI solutions. You will oversee the complete lifecycle: architecting data pipelines, selecting optimal representations, running statistical analyses, and continuously refining models through iterative feedback and retraining loops. As a technical leader and primary subject matter expert, you will guide test and evaluation protocols, evaluate data distribution shifts, and ensure our AI systems deliver accurate, mission-critical predictions at scale. The annual base salary range for this role is $188,000-$223,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things. Applied Machine Learning: Demonstrated experience selecting, training, evaluating, and productizing AI/ML models using standard frameworks (e.g., PyTorch, TensorFlow) and ML libraries. Model Lifecycle & MLOps: Hands-on experience in pipeline interaction, data modeling, feature selection, model verification/validation, monitoring, and iterative retraining based on feedback loops. Data Engineering & Statistical Analysis: Strong capabilities in statistical analysis, detecting data distribution shifts, and converting raw datasets into effective data representations for ML algorithms. Prototype-to-Production Engineering: Proven ability to transform experimental data science prototypes into scalable, performant production systems with proper system integration oversight. Software Foundations & Test Protocols: Sound understanding of application development concepts, data structures, software architecture, and running standard test/evaluation protocols. Communication & Technical Leadership: Excellent analytical, problem-solving, and presentation skills to serve as a primary POC for AI initiatives and guide cross-functional teams. Education: Bachelor’s Degree in Computer Science, Computer Engineering, Software Engineering, or a related technical discipline, PLUS 10 + years of professional software engineering experience OR Master's Degree PLUS 5+ years of professional software engineering experience OR H igh School Diploma / GED , PLUS 15+ years of hands-on technical software development experience in lieu of a degree. Multi-Language Mastery: Advanced proficiency in multiple programming languages, such as Python, Java, C, C++, or R. Advanced MLOps & Infrastructure: Deep familiarity with cloud-based AI deployments, automated model scheduling, data governance, and automated CI/CD pipelines for ML models. Distributed Team Leadership: Direct experience leading geographically dispersed engineering teams and driving collaborative AI research initiatives.

Requirements

Must-haves

ACTIVE SECURITY CLEARANCE AT THE TS/SCI POLYGRAPH LEVEL IS REQUIRED
Applied Machine Learning: Demonstrated experience selecting, training, evaluating, and productizing AI/ML models using standard frameworks (e.g., PyTorch, TensorFlow) and ML libraries.
Model Lifecycle & MLOps: Hands-on experience in pipeline interaction, data modeling, feature selection, model verification/validation, monitoring, and iterative retraining based on feedback loops.
Data Engineering & Statistical Analysis: Strong capabilities in statistical analysis, detecting data distribution shifts, and converting raw datasets into effective data representations for ML algorithms.
Prototype-to-Production Engineering: Proven ability to transform experimental data science prototypes into scalable, performant production systems with proper system integration oversight.
Software Foundations & Test Protocols: Sound understanding of application development concepts, data structures, software architecture, and running standard test/evaluation protocols.
Communication & Technical Leadership: Excellent analytical, problem-solving, and presentation skills to serve as a primary POC for AI initiatives and guide cross-functional teams.
Multi-Language Mastery: Advanced proficiency in multiple programming languages, such as Python, Java, C, C++, or R.
Advanced MLOps & Infrastructure: Deep familiarity with cloud-based AI deployments, automated model scheduling, data governance, and automated CI/CD pipelines for ML models.
Distributed Team Leadership: Direct experience leading geographically dispersed engineering teams and driving collaborative AI research initiatives.

Nice-to-haves

None flagged.

Application prep

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Keywords to mirror

machine learningaimlpythonjavaci/cd

Platform: agile-ats
Effort: medium

Benefits

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Vision