AI/ML Engineer - Geospatial (TS/SCI)

Title: AI/Machine Learning Engineer – Vision Language Models / Multimodal AI (NGA)Location: Springfield or Herndon, VA (onsite)Clearance: TS/SCI (CI Poly preferred)Position Type: Full-Time, Direct HirePay: $175,000 to $250,000 for an SMECompany: 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.Disclaimer: We are unable to provide work sponsorship for this roleOverview:We'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.Huge 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.Key Responsibilities:Design and execute fine-tuning pipelines for Vision Language Models (VLMs) using domain-specific imagery datasetsHandle data preprocessing, training orchestration, and hyperparameter optimization for multimodal modelsBuild evaluation frameworks for image understanding, visual question answering, and spatial reasoning tasksDevelop scalable AWS-based ML infrastructure using SageMaker and GPU-enabled EC2 for distributed trainingCreate data pipelines for curating, annotating, and transforming geospatial imagery into model-ready datasetsPartner with applied scientists and architects on model architecture improvements, LoRA/QLoRA strategies, and inference optimizationRequired Qualifications:Active TS/SCI with CI Poly5+ years of machine learning engineering experience focused on deep learning1+ year of hands-on experience fine-tuning foundation models (LLMs or VLMs)Experience with LoRA, QLoRA, adapters, supervised fine-tuning, instruction tuning, and RLHF/DPO4+ years of advanced Python development for ML workloadsStrong PyTorch and HuggingFace experience (Transformers, PEFT, Datasets, Accelerate)Experience with distributed training frameworks such as DeepSpeed, FSDP, or Megatron3+ years working with computer vision or multimodal modelsFamiliarity with vision transformer architectures (ViT, CLIP, LLaVA, etc.)Experience processing and augmenting image datasets at scale3+ years with AWS ML infrastructure including SageMaker, EC2 GPU environments, and S3Experience with ML evaluation pipelines, benchmarking, metrics, and result analysisStrong software engineering fundamentals including version control, testing, and CI/CDPreferred Qualifications:2+ years working with geospatial or remote sensing imageryExperience with EO or SAR satellite imageryUnderstanding of geospatial metadata, coordinate systems, and imagery preprocessingExperience with model quantization / inference optimization (vLLM, TensorRT, ONNX)MLOps tooling experience (MLflow, Weights & Biases, SageMaker Experiments)Familiarity with annotation tools and active learning workflowsContainerized ML experience with Docker / ECR / ECS / EKSExperience supporting ATO processes and NIST 800-53 complianceExperience deploying in air-gapped/disconnected environmentsFamiliarity with multimodal evaluation benchmarks (MMMU, MMBench, GQA)Publications or contributions in computer vision, multimodal AI, or VLMsSynthetic data generation experience for training augmentation#NGA #AI #MachineLearning #TSSCI #AIEngineer #DataScience #Geospatial