Senior Vision-Language Model Engineer

Aqua IT; Springfield (VA); In loco; USD 150.000 - 190.000; Tempo pieno

Descrizione del lavoro

Aqua IT is seeking a seasoned ML Engineer to design and run fine-tuning pipelines for Vision-Language Models on domain imagery. You will build multimodal evaluation frameworks and scalable AWS-based training infrastructure for distributed fine-tuning of large models.

You will curate geospatial datasets, collaborate on architectures and adapters, and optimize inference. Strong Python, PyTorch, and HuggingFace expertise are essential for success.

Competenze

• 5+ years of professional ML 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.

Mansioni

• Design and execute fine-tuning pipelines for Vision-Language Models on domain-specific imagery datasets.
• Develop evaluation frameworks for multimodal model performance across image understanding, VQA, and spatial reasoning.
• Build scalable training infrastructure on AWS (SageMaker, EC2 GPU instances) for distributed fine-tuning of large multimodal models.
• Engineer data pipelines to curate, annotate, and transform geospatial imagery datasets for supervised and instruction-tuning workflows.
• Collaborate with applied scientists and solutions architects to iterate on architectures, adapter strategies (LoRA/QLoRA), and inference optimization.

Conoscenze

Python development

PyTorch

HuggingFace ecosystem

Distributed training

Model fine-tuning

CI/CD for ML

Strumenti

AWS SageMaker

DeepSpeed

FSDP

Megatron

TensorRT

ONNX

Descrizione del lavoro

Aqua IT is seeking a seasoned ML Engineer to design and run fine-tuning pipelines for Vision-Language Models on domain imagery. You will build multimodal evaluation frameworks and scalable AWS-based training infrastructure for distributed fine-tuning of large models.

You will curate geospatial datasets, collaborate on architectures and adapters, and optimize inference. Strong Python, PyTorch, and HuggingFace expertise are essential for success.