VLM Engineer

Overview Location: Springfield, VA OR Reston, VA

Clearance Required: Active TS clearance (with SCI Eligibility) and CI Poly

At Bcore, our strength comes from how we deliver impact to the mission. Whether it’s architecting critical IT solutions, producing actionable intelligence, or developing cutting edge technology, we succeed because of the expertise, collaboration, and agility of our teams. Our Mission Services division combines enterprise IT, cloud solutions, Dev Sec Ops , systems engineering, software development, and operational support. Bcore accelerates decisive advantage for warfighters and intelligence professionals by fusing human insight, rapid-fire engineering, precision-measured outcomes, and relentless grit into mission-ready solutions.

Do you want to join a team that is building tailored technical solutions to modernize our government’s mission and our client’s business? Do you have a desire to change how people work? Are you interested in helping to protect our nation’s cyber interests? Join our growing team as a VLM Engineer
.

• 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

Clearance Required: Active TS clearance (with SCI Eligibility) and CI Poly

Education/

Experience:

• Requires Bachelor's degree
• 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)
• 4+ years of advanced Python development for ML workloads
• 3+ years of experience with computer vision or multimodal models
• 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

• Experience with parameter‑efficient fine‑tuning methods (LoRA, QLoRA, adapters)
• Familiarity with supervised fine‑tuning, instruction tuning, and RLHF/DPO alignment techniques
• Strong proficiency with PyTorch and the Hugging Face ecosystem (Transformers, PEFT, Datasets, Accelerate)
• Experience with distributed training frameworks (Deep Speed, FSDP, or Megatron)
• Understanding of vision transformer architectures (ViT, CLIP, LLaVA‑family models, or similar)
• Experience processing and augmenting image datasets at scale
• Strong software engineering fundamentals (version control, testing, CI/CD for ML workflows)

What is ideal?

• 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 workflows (Docker, ECR, ECS/EKS)
• 2+ years of experience with Authority to Operate (ATO) processes in government environments
• Implementation of NIST…