Software Engineer II

Role Overview

• 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

Requirements

• REQUIRED
• MUST have a current TS/SCI Polygraph clearance to apply for role.
• 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 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)

Tech Stack

• AWS
• EC2
• Node.js
• Python
• PyTorch
• TypeScript

Benefits

• Medical/Dental/Vision ( 100% Employer Paid Medical Plan )
• Short/Long Term Disability (Employer Paid)
• Life Insurance ( Employer Paid )
• Yearly $5,000 towards education/training/certification.
• Employees are in control of their career path through our Career Pathway Program.
• Employer paid Company Vacation Package for you and a guest !
• Retirement: Quevera will match up to 6% towards your 401K and an additional 4% profit sharing!