Software Engineer II

About the role

Quevera is seeking a highly skilled Software Engineer II with an active TS/SCI clearance with Polygraph to support mission-critical programs. The role involves developing and optimizing advanced machine learning solutions supporting multimodal artificial intelligence and computer vision applications for national security missions.

Responsibilities

• Design and execute fine-tuning pipelines for Vision-Language Models (VLMs) using domain-specific imagery datasets.
• Develop data preprocessing, training orchestration, and hyperparameter optimization workflows.
• Build and implement evaluation frameworks for multimodal model performance, including image understanding, visual question answering, and spatial reasoning.
• Develop scalable distributed training infrastructure using AWS services, including SageMaker and EC2 GPU instances.
• Engineer data pipelines for curating, annotating, and transforming geospatial imagery into model-ready datasets.
• Collaborate with applied scientists and solutions architects to optimize model architectures and parameter-efficient fine-tuning strategies, including LoRA and QLoRA.
• Optimize model inference performance and deployment workflows.
• Develop secure, scalable machine learning solutions that support mission requirements.

Requirements

• Active TS/SCI clearance with Polygraph required.
• Current NGA eligibility with active SBU, SECNet, and COE accounts.
• Five (5) or more years of professional machine learning engineering experience with a focus on deep learning.
• One (1) or more years of experience fine-tuning large language models (LLMs) or Vision-Language Models (VLMs).
• Experience with parameter-efficient fine-tuning techniques, including LoRA, QLoRA, and adapters.
• Familiarity with supervised fine-tuning, instruction tuning, and RLHF/DPO alignment techniques.
• Four (4) or more years of advanced Python development for machine learning workloads.
• Strong proficiency with PyTorch and the Hugging Face ecosystem, including Transformers, PEFT, Datasets, and Accelerate.
• Experience with distributed training frameworks such as DeepSpeed, FSDP, or Megatron.
• Three (3) or more years of experience with computer vision or multimodal AI models.
• Understanding of Vision Transformer architectures, including ViT, CLIP, LLaVA, or similar models.
• Experience processing and augmenting image datasets at scale.
• Three (3) or more years of experience with AWS machine learning infrastructure, including SageMaker, EC2 GPU instances, and Amazon S3.
• Experience building machine learning evaluation pipelines, including automated benchmarking, metric computation, and result analysis.
• Strong software engineering fundamentals, including version control, testing, and CI/CD practices for machine learning workflows.

Qualifications

• Five (5) or more years of professional machine learning engineering experience with a focus on deep learning.
• One (1) or more years of experience fine-tuning large language models (LLMs) or Vision-Language Models (VLMs).
• Experience with parameter-efficient fine-tuning techniques, including LoRA, QLoRA, and adapters.
• Familiarity with supervised fine-tuning, instruction tuning, and RLHF/DPO alignment techniques.
• Four (4) or more years of advanced Python development for machine learning workloads.
• Strong proficiency with PyTorch and the Hugging Face ecosystem, including Transformers, PEFT, Datasets, and Accelerate.
• Experience with distributed training frameworks such as DeepSpeed, FSDP, or Megatron.
• Three (3) or more years of experience with computer vision or multimodal AI models.
• Understanding of Vision Transformer architectures, including ViT, CLIP, LLaVA, or similar models.
• Experience processing and augmenting image datasets at scale.
• Three (3) or more years of experience with AWS machine learning infrastructure, including SageMaker, EC2 GPU instances, and Amazon S3.
• Experience building machine learning evaluation pipelines, including automated benchmarking, metric computation, and result analysis.
• Strong software engineering fundamentals, including version control, testing, and CI/CD practices for machine learning workflows.

Skills

• Advanced Python development for machine learning workloads.
• Experience with PyTorch and the Hugging Face ecosystem.
• Experience with distributed training frameworks such as DeepSpeed, FSDP, or Megatron.
• Experience with computer vision or multimodal AI models.
• Experience with Vision Transformer architectures.
• Experience with AWS machine learning infrastructure.
• Experience building machine learning evaluation pipelines.
• Software engineering fundamentals, including version control, testing, and CI/CD practices.

Benefits

At Quevera, we offer:

• 100% employer-paid medical coverage (optional plan)
• Competitive options for Medical, Dental and Vision insurance
• Employer-paid short-term and long-term disability coverage
• Employer-paid life insurance
• $5,000 annually for education, training, certifications, and professional development
• Career advancement through our structured IQWay Program
• Up to 6% 401(k) match
• Additional 4% profit-sharing contribution

Pay

Salary range: $120,000 - $150,000 per year

Schedule

Must be willing to work onsite in a SCIF daily, or as required.