Senior Systems Engineer – Clinical NLP Research Support

Senior Systems Engineer – Clinical NLP Research Support

Aptive Resources is seeking a Senior Systems Engineer to support a Department of Veterans Affairs (VA) Office of Research and Development (ORD) research study at the Durham VA Health Care System.

The study is the largest gene-by-environment genome-wide interaction study (GEWIS) of suicidal thoughts and behaviors to date, drawing on data from the Million Veteran Program (MVP). Its objective is to discover new, replicable gene-by-environment interactions that increase Veterans' risk, and its long-term goal is to inform screening and intervention strategies that reduce suicide and suicidal behavior among Veterans. The Senior Systems Engineer will support this work by turning VA electronic health record (EHR) clinical text into validated, analysis-ready data for the study team.

The ideal candidate will have significant experience developing artificial intelligence/super intelligence (AI/SI) methods for clinical or EHR text evaluation and leading the full technical lifecycle of population-scale text-processing pipelines in a healthcare or research environment, such as VA.

This is a part-time (20 hours/week), 100% remote position. It is currently funded through September 29, 2027 under the current contract period of performance, with potential for extension into the next option year based on contract continuation and program needs.

• Develop methods and infrastructure, and refine natural language processing (NLP) and large language model (LLM) approaches, to identify predictor variables for suicidal thoughts and behaviors in VA electronic health record (EHR) clinical notes, under the direction of the study team.
• Build and maintain custom tokenization and text-processing pipelines that run at population scale across the Million Veteran Program (MVP) cohort.
• Extract and validate phenotypes (suicidal ideation, suicide attempts, and related exposures) for the study's gene-by-environment analyses.
• Prepare, document, and transfer analysis-ready datasets to the study's statistical genetics team.
• Manage technical lifecycle activities, including code version control, testing, documentation, and reproducibility within the VA research computing environment.
• Ensure all work complies with VA privacy and information security requirements for protected health information (PHI) and personally identifiable information (PII).
• Troubleshoot pipeline performance, scalability, and data quality issues, and recommend and implement improvements.
• Coordinate with the Principal Investigator and study team and provide regular progress updates against project milestones to support completion within the grant period.
• Prepare technical documentation, workflow documentation, and handoff materials.

• Bachelor's degree in computer science, engineering, data science, informatics, or a related technical field.
• 8+ years of experience in computer systems engineering, software development, data engineering, or technical implementation.
• Experience with AI/SI methods development, including developing LLM models, for EHR text evaluation within a federal research environment.
• Experience with the technical lifecycle of this type of research, including custom tokenization and population-scale work.
• Experience with programming and data pipeline development.
• Experience working with protected health information (PHI) and personally identifiable information (PII) in accordance with VA privacy and security requirements.
• Ability to work independently in a part-time, remote role while meeting project deadlines.
• Ability to communicate effectively with both technical and research stakeholders.
• Ability to obtain and maintain a public trust clearance.
• Legal authorization to work in the United States.

• Significant experience developing NLP or LLM methods for VA.
• Experience supporting research studies, particularly in genomics, and familiarity with programs like the Million Veteran Program.
• Experience with transformer-based models, model fine-tuning, or custom tokenizer development.
• Experience developing or validating computable phenotypes, including chart review or annotation workflows.
• Experience with GPU-based or high-performance computing environments.
• Familiarity with mental health or suicide prevention research.
• Experience with version control, technical documentation, and reproducible workflows.
• High degree of professionalism, discretion, and mature judgment in handling sensitive information.