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Software Engineer II
QueveraSoftware Engineer II designing fine-tuning pipelines for Vision-Language Models in a leading ML company. Engaging in collaborative data processing, model building, and evaluation frameworks.
Tech Stack
Tools & technologiesAWSEC2Node.jsPythonPyTorchTypeScript
About the role
Key responsibilities & impact- 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
Requirements
What you’ll need- REQUIRED - MUST have a current TS/SCI Polygraph clearance to apply for role.
- 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)
Benefits
Comp & perks- Medical/Dental/Vision ( 100% Employer Paid Medical Plan )
- Short/Long Term Disability (Employer Paid)
- Life Insurance ( Employer Paid )
- Yearly $5,000 towards education/training/certification.
- Employees are in control of their career path through our Career Pathway Program.
- Employer paid Company Vacation Package for you and a guest !
- Retirement: Quevera will match up to 6% towards your 401K and an additional 4% profit sharing!
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
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Hard Skills & Tools
fine-tuning pipelinesVision-Language Modelshyperparameter optimizationevaluation frameworksdeep learningparameter-efficient fine-tuningPythonPyTorchcomputer visionML evaluation pipelines
Soft Skills
collaborationproblem-solvingcommunication
Certifications
TS/SCI Polygraph clearanceNGA eligibility