> Markdown version of [/jobs/ext/1379997-machine-learning-engineer-geospatial-ts-sci](https://www.wearedevelopers.com/jobs/ext/1379997-machine-learning-engineer-geospatial-ts-sci). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Engineer - Geospatial (TS/SCI) - **Company:** LaunchCode - **Location:** Herndon, VA, United States - **Salary:** $175,000.0 - $250,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Computer Vision, Continuous Integration, Data Cleansing, Distributed Computing Environment, Machine Learning, Language Modeling, Pytorch, Large Language Models, Deep Learning, Question Answering, ONNX (Open Neural Network Exchange) Format, HuggingFace, Machine Learning Operations, TensorRT, Software Version Control, Data Pipelines, Docker, Data Generation - **Published:** July 22, 2026 - **Apply:** https://www.clearancejobs.com/jobs/9045884/machine-learning-engineer-geospatial-tssci ## About the Role 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., * Active TS/SCI with CI Poly * 5+ years of machine learning engineering experience focused on deep learning * 1+ year of hands-on experience fine-tuning foundation models (LLMs or VLMs) * Experience with LoRA, QLoRA, adapters, supervised fine-tuning, instruction tuning, and RLHF/DPO * 4+ years of advanced Python development for ML workloads * Strong PyTorch and HuggingFace experience (Transformers, PEFT, Datasets, Accelerate) * Experience with distributed training frameworks such as DeepSpeed, FSDP, or Megatron * 3+ years working with computer vision or multimodal models * Familiarity with vision transformer architectures (ViT, CLIP, LLaVA, etc.) * Experience processing and augmenting image datasets at scale * 3+ years with AWS ML infrastructure including SageMaker, EC2 GPU environments, and S3 * Experience with ML evaluation pipelines, benchmarking, metrics, and result analysis * Strong software engineering fundamentals including version control, testing, and CI/CD, * 2+ years working with geospatial or remote sensing imagery * Experience with EO or SAR satellite imagery * Understanding of geospatial metadata, coordinate systems, and imagery preprocessing * Experience with model quantization / inference optimization (vLLM, TensorRT, ONNX) * MLOps tooling experience (MLflow, Weights & Biases, SageMaker Experiments) * Familiarity with annotation tools and active learning workflows * Containerized ML experience with Docker / ECR / ECS / EKS * Experience supporting ATO processes and NIST 800-53 compliance * Experience deploying in air-gapped/disconnected environments * Familiarity with multimodal evaluation benchmarks (MMMU, MMBench, GQA) * Publications or contributions in computer vision, multimodal AI, or VLMs * Synthetic data generation experience for training augmentation ## Description * Design and execute fine-tuning pipelines for Vision Language Models (VLMs) using domain-specific imagery datasets * Handle data preprocessing, training orchestration, and hyperparameter optimization for multimodal models * Build evaluation frameworks for image understanding, visual question answering, and spatial reasoning tasks * Develop scalable AWS-based ML infrastructure using SageMaker and GPU-enabled EC2 for distributed training * Create data pipelines for curating, annotating, and transforming geospatial imagery into model-ready datasets * Partner with applied scientists and architects on model architecture improvements, LoRA/QLoRA strategies, and inference optimization ## Related Videos - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Geometric deep learning for drug discovery](https://www.wearedevelopers.com/videos/264-geometric-deep-learning-for-drug-discovery) - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [The Best Large Language Models on The Market](https://www.wearedevelopers.com/magazine/319-the-best-large-language-models-on-the-market) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production)