Insight Global is seeking a Machine Learning Engineer to support a Startup Technology Company! This opportunity is based out of Charlotte, NC, and will be on site. Additionally, this opportunity offers quick interviews, competitive rates (ranging from $100K-$160K based off of years of experience) and there is lots of stability and room for growth, as this is a direct hire opportunity.
Must Haves:
- Ability to work on site in Charlotte, NC 5x a week
- Ability to work on a W2 Basis without sponsorship
- Experience working at a Startup Client
- Experience working with a SaaS company
- 3+ years' experience with NLP frameworks (spaCy, Transformers, NLTK)
- Proficiency in Python and ML libraries (scikit-learn, PyTorch/TensorFlow)
- Experience with intent classification, named entity recognition, and text parsing
- Understanding of optimization algorithms and constraint satisfaction problems
- Familiarity with API development and deployment (FastAPI, Flask)
- Strong grasp of sports scheduling concepts and common constraints
- Experience translating business requirements into technical specifications
- Understanding of data structures and algorithmic complexity
- Excellent problem-solving and analytical thinking
- Strong communication skills for cross-functional collaboration
- Attention to detail in handling edge cases and ambiguous inputs
Preferred Skills:
- Experience with sports analytics or scheduling systems
- Knowledge of linear programming and combinatorial optimization
- Familiarity with LLMs and prompt engineering
- Background in computational linguistics or related field
- Experience with A/B testing and model evaluation methodologies
Job Description:
- We're seeking an ML Engineer to develop and implement natural language processing systems that convert user-friendly constraint descriptions into structured data for our sports scheduling optimization engine. You'll bridge the gap between human intent and algorithmic execution.
Responsibilities:
Natural Language Processing (NLP) System Development
- Design and implement NLP models to parse natural language scheduling constraints
- Build robust intent classification and entity extraction systems
- Develop constraint validation and disambiguation workflows
- Create feedback loops to improve model accuracy over time
Data Structure Design
- Transform parsed constraints into structured representations for optimization algorithms
- Design flexible schemas that accommodate diverse scheduling requirements
- Ensure seamless integration between NLP outputs and scheduling engine inputs
Model Training & Optimization
- Curate and expand training datasets for sports scheduling domain
- Fine-tune language models for constraint understanding
- Implement evaluation metrics specific to constraint extraction accuracy
- Optimize model performance for real-time constraint processing
Integration & Deployment
- Build APIs for constraint ingestion and processing
- Implement monitoring and logging for production NLP systems
- Collaborate with backend engineers on scheduling algorithm integration
- Ensure system scalability and reliability
