Role overview
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