Sr. AWS Machine Learning Engineer [$365k/yr+] TS/SCI-CI Poly
SYSTOLIC, INC.
Springfield, United States of America
2 days ago
Role details
Contract type
Permanent contract Employment type
Full-time (> 32 hours) Working hours
Regular working hours Languages
English Experience level
SeniorJob location
Springfield, United States of America
Tech stack
Testing (Software)
Amazon Web Services (AWS)
Amazon Web Services (AWS)
Computer Vision
Continuous Delivery
Continuous Integration
Data Cleansing
Distributed Computing Environment
Python
Machine Learning
Language Modeling
Azure
PyTorch
Large Language Models
Deep Learning
GIT
Question Answering
Optimization Algorithms
Machine Learning Operations
Data Pipelines
Docker
Job description
- Designs and executes fine-tuning pipelines for Vision-Language Models on imagery datasets, including data preprocessing, training orchestration, and hyperparameter optimization.
- Develops and implements evaluation frameworks for multimodal model performance.
- Builds scalable training infrastructure on AWS, SageMaker, and EC2 GPU instances for distributed fine-tuning of large multimodal models.
- Engineers data pipelines for geospatial imagery datasets.
- Requires machine learning engineering experience with deep learning, fine-tuning LLMs or VLMs, Python, PyTorch, distributed training frameworks, computer vision, and AWS ML infrastructure.
- Applies strong software engineering fundamentals, Git for version control, software testing, and Continuous Integration/Continuous Delivery (CI/CD) for ML workflows.
- Experience with vLLM, MLOps, and Docker is beneficial., * 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
- Degree: Technical bachelor's degree or equivalent experience
- Years of experience: 7+ years
- Total Compensation: $365k+ yearly