Machine Learning Engineer

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