Computer Vision & AI/ML Engineer Jobs

Aqua IT Springfield, USFull-timeAbout the RoleDescription 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 optimizationDevelop and implement evaluation frameworks for multimodal model performance, including task-specific metrics for image understanding, visual question answering, and spatial reasoningBuild scalable training infrastructure on AWS (SageMaker, EC2 GPU instances) for distributed fine-tuning of large multimodal modelsEngineer data pipelines for curating, annotating, and transforming geospatial imagery datasets into model-ready formats for supervised and instruction-tuning workflowsCollaborate with applied scientists and solutions architects to iterate on model architectures, adapter strategies (LoRA/QLoRA), and inference optimization techniquesBasic RequirementsTS/SCI with CI Poly required with current NGA eligibility and SBU/SECNet/COE accountsMust 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 workloadsStrong 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 modelsUnderstanding 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 infrastructureSageMaker Training jobs, Processing jobs, and endpoint deploymentGPU 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 pipelinesAutomated benchmarking, metric computation, and result analysisExperience with both quantitative metrics and qualitative/human evaluation approachesStrong software engineering fundamentals (version control, testing, CI/CD for ML workflows)