VLM Engineer at teKnoluxion

Springfield, Virginia, United States -

Full Time

Start Date

Immediate

Expiry Date

28 Aug, 26

Salary

0.0

Posted On

30 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Vision-Language Models, PyTorch, HuggingFace, AWS SageMaker, LoRA/QLoRA, Computer Vision, Python, Distributed Training, DeepSpeed, FSDP, Vision Transformers, MLOps, Data Pipelines, Model Quantization, Geospatial Imagery, TS/SCI Clearance

Industry

Information Technology & Services

Description

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, DevSecOps, 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. Responsibilities What you get to do every day: 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 modelsEngineer 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 Qualifications 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 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 Required Skills: 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 HuggingFace ecosystem (Transformers, PEFT, Datasets, Accelerate) Experience with distributed training frameworks (DeepSpeed, 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, 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 What you can expect from us Recognizing great achievements do not go unnoticed by bcore through service anniversaries, spot awards, and employee referral bonuses You’ll join a growing organization of passionate, top-shelf, IT engineering professionals with extensive experience in actively developing the technology revolution in the Intelligence community Highlights of our benefits include Health/Dental/Vision, 401(k) match, Universal Leave, STD/LTD/Life Insurance/Voluntary Life Insurance, Stipends, Referral Bonuses, and more! Compensation is unique to each candidate and compensation packages are based on education, experience, and other requirements. BCore is proud to be an equal opportunity workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, sexual orientation or any other characteristic protected by law.

Responsibilities

Design and execute fine-tuning pipelines for Vision-Language Models using domain-specific imagery and build scalable training infrastructure on AWS. Collaborate with architects to optimize model architectures and develop evaluation frameworks for multimodal performance.