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SkillCorner Tech Stack
Computer vision platform extracting player tracking data from single camera feeds
SkillCorner runs a CV + ML pipeline (Python, TensorFlow, PyTorch, CUDA) that automatically extracts player and ball positions from video, now scaling across football, basketball, and American football. The stack—heavy on inference optimization (TensorRT, ONNX), cloud orchestration (AWS EKS, Lambda, Step Functions), and ML tooling (pytest, pre-commit, GitLab CI/CD)—reflects active effort to solve neural network cost and deployment velocity. Hiring is concentrated in data (5 roles) and engineering (4), with an AI Director search, signaling infrastructure maturation ahead of international expansion.
Tech Stack 25 technologies
Core StackPython TensorFlow PyTorch AWS AWS RDS AWS Lambda Docker Grafana Django Angular GitLab Rust Kubernetes GitHub ONNX TensorRT CUDA AWS EKS Git pre-commit CI/CD pytest AWS Step Functions Kaggle OpenCV
What SkillCorner Is Building
◆Challenges
- Scalable data processing
- Petabyte-scale data security
- Cloud infrastructure reliability
- Optimizing neural network inference cost
- Streamlining ml deployment
- Tracking fast-moving players while handling occlusions
- Expansion internationale
- Complexité multi-entités
- Revenue recognition saas
- High data volume under time pressure
▲Active Projects
- Design of simplified and robust data quality frameworks
- Prediction of performance adaptation when moving between competitions or leagues
- Video ingestion engine
- Client-facing api
- Modeling player physical progression over time
- Ml production tooling for reproducibility
- Ml lifecycle management
- Ml model optimization for cost reduction
- Advanced modeling of tracking data
- Design new football metrics
Hiring Activity
10 roles · 8 in 30d
Department
Seniority
Notable leadership hires: AI Director
Company intelligence
