Currently tracking 66 active AI roles, up 53% versus the prior 4 weeks. Primary focus: Agent · Engineering.
| Title | Stage | AI score |
|---|---|---|
| Research Engineer, Data Infrastructure Mistral AI is seeking a Research Engineer for their Data Infrastructure team to architect and scale massive distributed compute and storage systems for frontier model training and fine-tuning. The role involves building the backbone of their AI ecosystem, managing multi-cluster orchestration, designing future-proof storage solutions, and ensuring operational excellence for large-scale data platforms. | Data | 8 |
| Code Data Annotation Quality Specialist Mistral AI is seeking a Data Quality Specialist to join their Human Data Annotation team. This hybrid role involves reviewing and auditing code annotations against rubrics to ensure data quality for AI model training and evaluation, and also building, maintaining, and troubleshooting internal tooling for annotators. The role requires strong analytical skills, attention to detail, proficiency in programming languages, and experience with code agents and developer environments. |
| 7 |
| Data Annotation Quality Specialist This role focuses on creating and auditing human data annotations to ensure the quality and efficiency of data used for training and evaluating AI models. It involves collaborating with technical teams to refine annotation processes and guidelines. | Data | 5 |
| Research Engineer, Data Infrastructure This role focuses on building and operating the next generation of data infrastructure at Mistral AI, including massive compute fleets and storage systems for high performance and scalability. The role involves designing and scaling big data compute and storage platforms, ensuring secure and governed data access for MLOps and research, and taking full lifecycle ownership of pipelines and critical training jobs. | Data | 5 |
| Data Engineer Seeking a Data Engineer with an Analytics Engineering background to build, optimize, and maintain data infrastructure. The role involves working with large datasets to support AI model training, deployment pipelines, and feature stores, as well as enabling business users with data. Requires proficiency in Python, SQL, dbt, and cloud platforms. | Data | 5 |