Member of Technical Staff, Data Analysis and Evaluation
C
CohereAI Systems
WorldwideFull-TimeStaff
SalaryCA$250K - CA$535K; CA$250K – CA$535K • Offers Equity • Multiple Ranges; Canada - Toronto (GTA), Montreal: Base Salary CA$295K – CA$535K • Offers Equity; Canada: Base Salary CA$250K – CA$460K • Offers Equity; USA - California, New York and Washington: Base Salary $205K – $380K • Offers Equity; USA - All Other States: Base Salary $175K – $325K • Offers Equity
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Job Details
- Required Skills
- PythonSQLData AnalysisMachine LearningPyTorchTensorflowSoftware EngineeringLLM
Requirements
- Extremely strong software engineering skills
- Strong expertise in designing and conducting data collection tasks, including working with human annotators
- Strong statistical skills and experience evaluating scientific experiments related to data collection and model performance
- Experience analyzing datasets with respect to their quality, biases, and suitability for training ML models
- Hands-on experience training large language models (LLMs) on distributed training infrastructures
- Familiarity with evaluating and improving the generalizability and robustness of ML systems
- Proficiency in programming languages such as Python
- Proficiency in ML frameworks (e.g., PyTorch, TensorFlow, JAX)
- Excellent communication skills to collaborate effectively with cross-functional teams and present findings
- One or more papers at top-tier venues (such as NeurIPS, ICML, ICLR, AIStats, MLSys, JMLR, AAAI, Nature, COLING, ACL, EMNLP)
Responsibilities
- Design and oversee data collection tasks, including supporting human annotators and ensuring data quality
- Develop and apply statistical methods to evaluate the quality and reliability of datasets
- Analyze and assess the generalizability and robustness of ML systems across diverse use cases
- Collaborate with teams to improve dataset quality and model performance
- Train and fine-tune large language models (LLMs) on distributed training infrastructures
- Conduct experiments to evaluate model performance and identify areas for improvement
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