A leading AI research initiative is seeking to benchmark and improve model performance and training speed through human-led planning and implementation workflows
Requirements
- Strong Python engineering skills for model training and data handling
- Familiarity with Docker-based development environments
- Experience with reproducibility and benchmarking in ML research (preferred)
Responsibilities
- Draft detailed, executable natural language plans for ML-related Kaggle-style tasks
- Implement those plans in Python code within a provided Docker environment
- Validate implementations against original plans and identify discrepancies
Other
- 3+ years of experience in applied machine learning or MLE roles
- Detail-oriented approach to technical planning and code validation
- Comfortable working independently under strict compliance constraints
- Commitment: ~20 hours/week
- Duration: 4–6 weeks
