{"schemaVersion":"jobsearcher.job.v1","id":"2b79fb31593868181db694e5","url":"https://jobsearcher.com/jobs/2b79fb31593868181db694e5","canonicalUrl":"https://jobsearcher.com/jobs/2b79fb31593868181db694e5","title":"Machine Learning Engineer - Geospatial (TS/SCI)","description":"Machine Learning Engineer - Geospatial (TS/SCI) (Biotech)\r\nTitle: AI/Machine Learning Engineer - Vision Language Models / Multimodal AI (NGA)\r\nLocation: Springfield or Herndon, VA (onsite)\r\nClearance: TS/SCI (CI Poly preferred)\r\nPosition Type: Full-Time, Direct Hire\r\nPay: $175,000 to $250,000 for an SME\r\nCompany: The name of our partner organization will be disclosed during the interview process. This is not a direct role with LaunchCode; it is a position through LaunchCode, working with one of our partner companies.\r\nDisclaimer: We are unable to provide work sponsorship for this role\r\nOverview\r\nWe're hiring a AI/Machine Learning Engineer with strong experience in multimodal AI and large-scale model training to support advanced vision-language initiatives in a secure government environment. This role will focus on fine-tuning Vision Language Models (VLMs) on domain-specific geospatial imagery, building scalable AWS training infrastructure, and developing evaluation frameworks for image understanding and spatial reasoning. Ideal candidates will have deep experience with PyTorch, HuggingFace, distributed training, and computer vision, along with the ability to optimize and deploy multimodal models in mission-critical environments.\r\nHuge plus for candidates who have hands-on experience taking multimodal models such as CLIP, LLaVA, Qwen-VL, or similar Vision Language Models and fine-tuning them on classified or mission-specific imagery datasets. The ideal candidate can build the AWS infrastructure needed to train and scale these models, evaluate performance improvements across real-world use cases, and deploy solutions into secure government or air-gapped environments.\r\nKey Responsibilities\r\nDesign and execute fine-tuning pipelines for Vision Language Models (VLMs) using domain-specific imagery datasets\r\nHandle data preprocessing, training orchestration, and hyperparameter optimization for multimodal models\r\nBuild evaluation frameworks for image understanding, visual question answering, and spatial reasoning tasks\r\nDevelop scalable AWS-based ML infrastructure using SageMaker and GPU-enabled EC2 for distributed training\r\nCreate data pipelines for curating, annotating, and transforming geospatial imagery into model-ready datasets\r\nPartner with applied scientists and architects on model architecture improvements, LoRA/QLoRA strategies, and inference optimization\r\nRequired Qualifications\r\nActive TS/SCI with CI Poly\r\n5+ years of machine learning engineering experience focused on deep learning\r\n1+ year of hands-on experience fine-tuning foundation models (LLMs or VLMs)\r\nExperience with LoRA, QLoRA, adapters, supervised fine-tuning, instruction tuning, and RLHF/DPO\r\n4+ years of advanced Python development for ML workloads\r\nStrong PyTorch and HuggingFace experience (Transformers, PEFT, Datasets, Accelerate)\r\nExperience with distributed training frameworks such as DeepSpeed, FSDP, or Megatron\r\n3+ years working with computer vision or multimodal models\r\nFamiliarity with vision transformer architectures (ViT, CLIP, LLaVA, etc.)\r\nExperience processing and augmenting image datasets at scale\r\n3+ years with AWS ML infrastructure including SageMaker, EC2 GPU environments, and S3\r\nExperience with ML evaluation pipelines, benchmarking, metrics, and result analysis\r\nStrong software engineering fundamentals including version control, testing, and CI/CD\r\nPreferred Qualifications\r\n2+ years working with geospatial or remote sensing imagery\r\nExperience with EO or SAR satellite imagery\r\nUnderstanding of geospatial metadata, coordinate systems, and imagery preprocessing\r\nExperience with model quantization / inference optimization (vLLM, TensorRT, ONNX)\r\nMLOps tooling experience (MLflow, Weights & Biases, SageMaker Experiments)\r\nFamiliarity with annotation tools and active learning workflows\r\nContainerized ML experience with Docker / ECR / ECS / EKS\r\nExperience supporting ATO processes and NIST 800-53 compliance\r\nExperience deploying in air-gapped/disconnected environments\r\nFamiliarity with multimodal evaluation benchmarks (MMMU, MMBench, GQA)\r\nPublications or contributions in computer vision, multimodal AI, or VLMs\r\nSynthetic data generation experience for training augmentation\r\nJ-18808-Ljbffr","company":"Launchcode Foundation In","rawCompany":"launchcode foundation in","city":"Springfield","state":"VA","isRemote":false,"isActive":true,"createdAt":"2026-08-12T00:51:27.000Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"19-2099.01","title":"Remote Sensing Scientists and Technologists","slug":"remote-sensing-scientists-and-technologists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"}],"industries":[{"code":"541990","title":"All Other Professional, Scientific, and Technical Services","slug":"all-other-professional-scientific-and-technical-services"},{"code":"541690","title":"Other Scientific and Technical Consulting Services","slug":"other-scientific-and-technical-consulting-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Machine Learning Engineer - Geospatial (TS/SCI)","description":"Machine Learning Engineer - Geospatial (TS/SCI) (Biotech)\r\nTitle: AI/Machine Learning Engineer - Vision Language Models / Multimodal AI (NGA)\r\nLocation: Springfield or Herndon, VA (onsite)\r\nClearance: TS/SCI (CI Poly preferred)\r\nPosition Type: Full-Time, Direct Hire\r\nPay: $175,000 to $250,000 for an SME\r\nCompany: The name of our partner organization will be disclosed during the interview process. This is not a direct role with LaunchCode; it is a position through LaunchCode, working with one of our partner companies.\r\nDisclaimer: We are unable to provide work sponsorship for this role\r\nOverview\r\nWe're hiring a AI/Machine Learning Engineer with strong experience in multimodal AI and large-scale model training to support advanced vision-language initiatives in a secure government environment. This role will focus on fine-tuning Vision Language Models (VLMs) on domain-specific geospatial imagery, building scalable AWS training infrastructure, and developing evaluation frameworks for image understanding and spatial reasoning. Ideal candidates will have deep experience with PyTorch, HuggingFace, distributed training, and computer vision, along with the ability to optimize and deploy multimodal models in mission-critical environments.\r\nHuge plus for candidates who have hands-on experience taking multimodal models such as CLIP, LLaVA, Qwen-VL, or similar Vision Language Models and fine-tuning them on classified or mission-specific imagery datasets. The ideal candidate can build the AWS infrastructure needed to train and scale these models, evaluate performance improvements across real-world use cases, and deploy solutions into secure government or air-gapped environments.\r\nKey Responsibilities\r\nDesign and execute fine-tuning pipelines for Vision Language Models (VLMs) using domain-specific imagery datasets\r\nHandle data preprocessing, training orchestration, and hyperparameter optimization for multimodal models\r\nBuild evaluation frameworks for image understanding, visual question answering, and spatial reasoning tasks\r\nDevelop scalable AWS-based ML infrastructure using SageMaker and GPU-enabled EC2 for distributed training\r\nCreate data pipelines for curating, annotating, and transforming geospatial imagery into model-ready datasets\r\nPartner with applied scientists and architects on model architecture improvements, LoRA/QLoRA strategies, and inference optimization\r\nRequired Qualifications\r\nActive TS/SCI with CI Poly\r\n5+ years of machine learning engineering experience focused on deep learning\r\n1+ year of hands-on experience fine-tuning foundation models (LLMs or VLMs)\r\nExperience with LoRA, QLoRA, adapters, supervised fine-tuning, instruction tuning, and RLHF/DPO\r\n4+ years of advanced Python development for ML workloads\r\nStrong PyTorch and HuggingFace experience (Transformers, PEFT, Datasets, Accelerate)\r\nExperience with distributed training frameworks such as DeepSpeed, FSDP, or Megatron\r\n3+ years working with computer vision or multimodal models\r\nFamiliarity with vision transformer architectures (ViT, CLIP, LLaVA, etc.)\r\nExperience processing and augmenting image datasets at scale\r\n3+ years with AWS ML infrastructure including SageMaker, EC2 GPU environments, and S3\r\nExperience with ML evaluation pipelines, benchmarking, metrics, and result analysis\r\nStrong software engineering fundamentals including version control, testing, and CI/CD\r\nPreferred Qualifications\r\n2+ years working with geospatial or remote sensing imagery\r\nExperience with EO or SAR satellite imagery\r\nUnderstanding of geospatial metadata, coordinate systems, and imagery preprocessing\r\nExperience with model quantization / inference optimization (vLLM, TensorRT, ONNX)\r\nMLOps tooling experience (MLflow, Weights & Biases, SageMaker Experiments)\r\nFamiliarity with annotation tools and active learning workflows\r\nContainerized ML experience with Docker / ECR / ECS / EKS\r\nExperience supporting ATO processes and NIST 800-53 compliance\r\nExperience deploying in air-gapped/disconnected environments\r\nFamiliarity with multimodal evaluation benchmarks (MMMU, MMBench, GQA)\r\nPublications or contributions in computer vision, multimodal AI, or VLMs\r\nSynthetic data generation experience for training augmentation\r\nJ-18808-Ljbffr","datePosted":"2026-08-12T00:51:27.000Z","dateModified":"2026-08-12T00:51:27.000Z","hiringOrganization":{"@type":"Organization","name":"Launchcode Foundation In","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Springfield","addressRegion":"VA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"2b79fb31593868181db694e5"},"url":"https://jobsearcher.com/jobs/2b79fb31593868181db694e5"}}