Computer Vision & AI/ML Engineer Jobs

Description of Services/Responsibilities: • 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 Basic Requirements • TS/SCI with CI Poly required with current NGA eligibility and SBU/SECNet/COE accounts • Must be willing to work in SCIF daily or as needed • 5+ years of professional machine learning engineering experience with a focus on deep learning • 1+ years of hands-on experience fine-tuning large foundation models (LLMs or VLMs) • Experience with parameter-efficient fine-tuning methods (LoRA, QLoRA, adapters) • Familiarity with supervised fine-tuning, instruction tuning, and RLHF/DPO alignment techniques • 4+ years of advanced Python development for ML workloads • Strong proficiency with PyTorch and the HuggingFace ecosystem (Transformers, PEFT, Datasets, Accelerate) • Experience with distributed training frameworks (DeepSpeed, FSDP, or Megatron) • 3+ years of experience with computer vision or multimodal models • Understanding of vision transformer architectures (ViT, CLIP, LLaVA-family models, or similar) • Experience processing and augmenting image datasets at scale • 3+ years of experience with AWS ML infrastructure SageMaker Training jobs, Processing jobs, and endpoint deployment GPU instance selection, multi-node training, and cost optimization on EC2 (P4/P5/G5/G6e) S3 data management for large-scale training datasets • 2+ years of experience building ML evaluation pipelines Automated benchmarking, metric computation, and result analysis Experience with both quantitative metrics and qualitative/human evaluation approaches • Strong software engineering fundamentals (version control, testing, CI/CD for ML workflows) Preferred Qualifications: • 2+ years of experience with geospatial or remote sensing imagery Familiarity with electro-optical and SAR satellite imagery formats and characteristics Understanding of geospatial metadata, coordinate systems, and imagery preprocessing • Experience with model quantization and inference optimization (vLLM, TensorRT, ONNX) Experience with MLOps and experiment tracking tools (MLflow, Weights & Biases, SageMaker Experiments) Familiarity with data annotation platforms and active learning workflows for imagery Experience with containerized ML workflows (Docker, ECR, ECS/EKS) 2+ years of experience with Authority to Operate (ATO) processes in government environments Implementation of NIST 800-53 controls and security compliance for ML systems • Experience deploying models in air-gapped or disconnected environments Familiarity with multimodal evaluation benchmarks (MMMU, MMBench, GQA, or domain-specific equivalents) Publications or demonstrated contributions in computer vision, VLMs, or multimodal AI Experience with synthetic data generation for training data augmentation Complete items below line after a partner is selected