Software Engineer II

Quevera; Herndon (VA); On-site; USD 120,000 - 150,000; Full time

Benefits offered by this job

Employer-paid medical/dental/vision plan (100% coverage)

401(k) match up to 6%

$5,000 yearly for education/training/certification

Job summary

Quevera, located in Herndon, Virginia, is looking for a Software Engineer II to develop pipelines for Vision-Language Models and enhance multimodal model performance using AWS infrastructure. Candidates should have significant machine learning experience, particularly in fine-tuning large models and a strong capability in computer vision.

The position offers many benefits including full coverage health plans and a retirement match program.

Qualifications

• 5+ years of professional machine learning engineering experience focused on deep learning.
• 5+ years of experience with AWS ML infrastructure including SageMaker.
• Strong proficiency with PyTorch and the HuggingFace ecosystem.

Responsibilities

• Design and execute fine-tuning pipelines for Vision-Language Models.
• Develop evaluation frameworks for multimodal model performance.
• Build scalable training infrastructure on AWS for distributed fine-tuning.

Skills

Deep learning expertise

Fine-tuning large models

Python development

Knowledge of AWS infrastructure

Computer vision

MLOps experience

Tools

PyTorch

AWS SageMaker

Docker

Job description

Job Description

Quevera is seeking a Software Engineer II to join our team. The role focuses on designing and executing fine‑tuning pipelines for Vision‑Language Models on domain‑specific imagery datasets, developing evaluation frameworks for multimodal model performance, and building scalable training infrastructure on AWS.

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 clearance with Polygraph and current NGA eligibility (SBU/SECNet/COE accounts).
• Must be willing to work in a 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/processing jobs, 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.

Benefits

• Employer‑paid medical/dental/vision plan (100% coverage).
• Employer‑paid short/long term disability.
• Employer‑paid life insurance.
• $5,000 yearly toward education/training/certification.
• Career Pathway Program.
• Retirement: 401(k) match up to 6% and additional 4% profit sharing.
• Company vacation package for you and a guest.

Quevera is an equal opportunity/affirmative action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected veteran status, age, or any other characteristic protected by law.