Software Engineer II

Job Description:

Quevera is seeking a Software Engineer II to join our team. At Quevera, we don't just offer jobs-we provide opportunities to be part of a dynamic, forward-thinking community that fosters innovation, collaboration, and personal growth.

You'll work with industry experts, take on exciting challenges, and have the creative freedom to build cutting-edge solutions, all while advancing your career in a space that truly values your skills and ideas.

HIGHLIGHT'S OF WORKING FOR QUEVERA:

Quevera employees voted Quevera as a TOP EMPLOYER in the Baltimore /DC area by the Washington for 2025 for the 5th consecutive year!

Excellent Quevera's 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!

REQUIRED - MUST have a current TS/SCI Polygraph clearance to apply for role. Only those with a current TS/SCI with Poly clearance will be considered.

Duties and 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 (Sage Maker, 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

Required Experience:

• 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 Hugging Face ecosystem (Transformers, PEFT, Datasets, Accelerate)
• Experience with distributed training frameworks (Deep Speed, 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
  Sage Maker 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)

Desired Experience:

• 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, Tensor
  
  RT, ONNX)
  
  Experience with MLOps and experiment tracking tools (MLflow, Weights & Biases, Sage Maker Experiments)
  Familiarity with data annotation platforms and active learning workflows for imagery
  
  Experience with containerized ML…