Role overview
**Summary**
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.
**Key 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
**Required Qualifications**
**Technical Skills**
* 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)
**Domain Knowledge**
* Strong grasp of sports scheduling concepts and common constraints
* Experience translating business requirements into technical specifications
* Understanding of data structures and algorithmic complexity
**Soft Skills**
* Excellent problem-solving and analytical thinking
* Strong communication skills for cross-functional collaboration
* Attention to detail in handling edge cases and ambiguous inputs
**Preferred Qualifications**
* 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
**Direct Placement Roles:**
**Compensation:**
**$180,000 to $200,000 per year annual salary.**
Exact compensation may vary based on several factors, including skills, experience, and education.
Benefit packages for this role include:
*Benefit packages for this role may include healthcare insurance offerings and paid leave as provided by applicable law.*