Job Requirements
Springfield, VA
Intel Agency (NSA, CIA, FBI, etc) CI Polygraph
Senior Level Career (10+ yrs experience)
Salary not specified
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Job Description
Candidates must already possess an active Top Secret/SCI w/ CI Polygraph to be considered.
Summary:
• Designs and executes fine-tuning pipelines for Vision-Language Models on imagery datasets, including data preprocessing, training orchestration, and hyperparameter optimization.
• Develops and implements evaluation frameworks for multimodal model performance.
• Builds scalable training infrastructure on AWS, SageMaker, and EC2 GPU instances for distributed fine-tuning of large multimodal models.
• Engineers data pipelines for geospatial imagery datasets.
• Requires machine learning engineering experience with deep learning, fine-tuning LLMs or VLMs, Python, PyTorch, distributed training frameworks, computer vision, and AWS ML infrastructure.
• Applies strong software engineering fundamentals, Git for version control, software testing, and Continuous Integration/Continuous Delivery (CI/CD) for ML workflows.
• Experience with vLLM, MLOps, and Docker is beneficial.
Qualifications & Compensation:
• Degree: Technical bachelor's degree or equivalent experience
• Years of experience: 7+ years
• Total Compensation: $365k+ yearly
Job Description:
• Design and execute fine-tuning pipelines for Vision-Language Models (VLMs) on domain-specific imagery datasets, including data preprocessing, training orchestration, and hyperparameter optimization.
• Develop and implement evaluation frameworks for multimodal model performance, including task-specific metrics for image understanding, visual question answering, and spatial reasoning.
• Build scalable training infrastructure on AWS (SageMaker, EC2 GPU instances) for distributed fine-tuning of large multimodal models.
• Engineer data pipelines for curating, annotating, and transforming geospatial imagery datasets into model-ready formats for supervised and instruction-tuning workflows.
• Collaborate with applied scientists and solutions architects to iterate on model architectures, adapter strategies (LoRA/QLoRA), and inference optimization techniques.
• Key Skills: Dataflow, AWS, LLM, Python, PyTorch, Software Testing, vLLM, Docker, Fine-tuning, Model Training, Amazon SageMaker, Machine Learning/Artificial Intelligence, Git, Continuous Integration, DevOps.
About SYSTOLIC:
SYSTOLIC is dedicated to giving our employees the best possible company experience so that they can focus on providing outstanding support to their customer’s mission. Our company is founded on integrity, enthusiasm, and a relentless commitment to supporting the Intelligence Community. You can learn more about us and submit an application to be considered against our current and future openings at https://systolic.com.
To learn about our compensation ranges, visit our Pay Transparency page at: https://systolic.com/pay-transparency
Summary:
• Designs and executes fine-tuning pipelines for Vision-Language Models on imagery datasets, including data preprocessing, training orchestration, and hyperparameter optimization.
• Develops and implements evaluation frameworks for multimodal model performance.
• Builds scalable training infrastructure on AWS, SageMaker, and EC2 GPU instances for distributed fine-tuning of large multimodal models.
• Engineers data pipelines for geospatial imagery datasets.
• Requires machine learning engineering experience with deep learning, fine-tuning LLMs or VLMs, Python, PyTorch, distributed training frameworks, computer vision, and AWS ML infrastructure.
• Applies strong software engineering fundamentals, Git for version control, software testing, and Continuous Integration/Continuous Delivery (CI/CD) for ML workflows.
• Experience with vLLM, MLOps, and Docker is beneficial.
Qualifications & Compensation:
• Degree: Technical bachelor's degree or equivalent experience
• Years of experience: 7+ years
• Total Compensation: $365k+ yearly
Job Description:
• Design and execute fine-tuning pipelines for Vision-Language Models (VLMs) on domain-specific imagery datasets, including data preprocessing, training orchestration, and hyperparameter optimization.
• Develop and implement evaluation frameworks for multimodal model performance, including task-specific metrics for image understanding, visual question answering, and spatial reasoning.
• Build scalable training infrastructure on AWS (SageMaker, EC2 GPU instances) for distributed fine-tuning of large multimodal models.
• Engineer data pipelines for curating, annotating, and transforming geospatial imagery datasets into model-ready formats for supervised and instruction-tuning workflows.
• Collaborate with applied scientists and solutions architects to iterate on model architectures, adapter strategies (LoRA/QLoRA), and inference optimization techniques.
• Key Skills: Dataflow, AWS, LLM, Python, PyTorch, Software Testing, vLLM, Docker, Fine-tuning, Model Training, Amazon SageMaker, Machine Learning/Artificial Intelligence, Git, Continuous Integration, DevOps.
About SYSTOLIC:
SYSTOLIC is dedicated to giving our employees the best possible company experience so that they can focus on providing outstanding support to their customer’s mission. Our company is founded on integrity, enthusiasm, and a relentless commitment to supporting the Intelligence Community. You can learn more about us and submit an application to be considered against our current and future openings at https://systolic.com.
To learn about our compensation ranges, visit our Pay Transparency page at: https://systolic.com/pay-transparency
group id: 10527119