Computer Vision & AI/ML Engineer Jobs

AIToolboard; Springfield (MA); On-site; USD 150,000 - 230,000; Full time

Job summary

Aqua IT Springfield, US is seeking a seasoned ML engineer to design and execute fine-tuning pipelines for Vision-Language Models on domain-specific imagery datasets, including data preprocessing, training orchestration, and hyperparameter optimization.

You will build evaluation frameworks for multimodal performance and prototype scalable AWS SageMaker training, collaborating to iterate on model architectures and optimization techniques.

Qualifications

• 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).
• Strong Python development for ML workloads (4+ years).
• Proficiency with PyTorch and HuggingFace ecosystem.

Responsibilities

• Design and execute fine-tuning pipelines for VLMs on domain imagery.
• Develop evaluation frameworks for multimodal model performance.
• Build scalable training infrastructure on AWS (SageMaker, EC2 GPU).
• Engineer data pipelines for geospatial imagery datasets for model-ready formats.
• Collaborate to iterate on model architectures and inference optimization.

Skills

Machine learning engineering

Python

PyTorch

HuggingFace

Distributed training

CI/CD for ML

Computer vision

Tools

SageMaker

EC2 GPU

DeepSpeed

Megatron

FSDP

Docker

ECR

ECS/EKS

TensorRT

ONNX

PyTorch

Weights & Biases

SageMaker Experiments

Job description

Aqua IT Springfield, US

Full-time

About the Role

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 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 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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