Must possess current TS/SCI Polygraph clearance for application
5+ years of professional machine learning engineering experience with deep learning focus
1+ years of experience fine-tuning large foundation models (LLMs or VLMs)
4+ years of advanced Python development for machine learning workloads
Strong proficiency in PyTorch and the HuggingFace ecosystem
3+ years of experience with AWS ML infrastructure
Experience with distributed training frameworks (DeepSpeed, FSDP, or Megatron)
Responsibilities
Design and execute fine-tuning pipelines for Vision-Language Models on domain-specific datasets
Develop and implement evaluation frameworks for multimodal model performance metrics
Build scalable training infrastructure on AWS for distributed fine-tuning of multimodal models
Engineer data pipelines for geospatial imagery datasets,currating and transforming them for model workflows
Collaborate with scientists and architects to iterate on model architectures and inference techniques
Benefits
Work within a secure SCIF environment
Opportunity to collaborate with leading scientists and engineers
Access to cutting-edge technologies and frameworks in machine learning
Engagement in high-impact projects related to geospatial intelligence
Professional growth and development opportunities in a niche field
Full Job Description
Job Description:
Quevera is seeking a Software Engineer II to join our team.
REQUIRED - MUST have a current TS/SCI Polygraph clearance toapply 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 (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
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 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)
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, 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 Complete items below line after a partner is selected
