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AI/ML Engineer (Computer Vision)

ChatGPT Jobs · Herndon, VA · 1 mo ago
EngineeringFull-time

Machine Learning & Artificial Intelligence

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.
  • 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.

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