What are the responsibilities and job description for the AI/ML Engineer (Computer Vision) position at ChatGPT Jobs?
Job Description
Job Title: (Not explicitly stated; inferred as Machine Learning Engineer specializing in VLMs)
Locations: Herndon, VA (On-site, Remote)
Responsibilities
Job Title: (Not explicitly stated; inferred as Machine Learning Engineer specializing in VLMs)
Locations: Herndon, VA (On-site, Remote)
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.
- 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).
- 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.