Member of Technical Staff, Data Analysis and Evaluation

About Cohere

Our mission is to scale intelligence to serve humanity. We’re training and deploying frontier models for developers and enterprises who are building AI systems to power magical experiences like content generation, semantic search, RAG, and agents. We believe that our work is instrumental to the widespread adoption of AI. We obsess over what we build and are a team of researchers, engineers, designers, and more, passionate about our craft.

Why this role?

As a Member of Technical Staff in Data Analysis and Evaluation, you will play a pivotal role in ensuring the quality, reliability, and performance of our large language models (LLMs). Your primary focus will be on designing and conducting data collection tasks, assessing and evaluating dataset quality, and analysing the robustness and generalisability of our models. You will work closely with cross-functional teams, including researchers, engineers, and data annotators, to conduct data-driven decision-making and improve the overall effectiveness of our AI systems. This role combines expertise in statistics, experimental design including human annotators, and machine learning to ensure that our models are trained on high-quality data and perform reliably across diverse scenarios. You will contribute to Cohere’s mission of advancing AI by ensuring our systems are robust, scalable, and impactful.

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.
• Analyse and assess the generalisability 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.

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 analysing 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 generalisability and robustness of ML systems.
• Proficiency in programming languages such as Python and 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).