Machine Learning Engineer

The Machine Learning Engineer will design, develop, and evaluate end-to-end machine learning solutions, focusing on large language models (LLMs), combining engineering rigor and research depth. They will lead the development of PoCs and applied research prototypes to explore novel AI capabilities, model interpretability, and safety strategies. Responsibilities include conducting cutting-edge experiments to assess model behavior, generalization, and fairness, as well as generating and curating synthetic and real-world datasets to optimize model robustness, reliability, and performance. The role involves fine-tuning and deploying large-scale models using prompt engineering, few-shot learning, and retrieval-augmented techniques. Collaboration with cross-functional teams to publish white papers, participate in conferences, and contribute to open-source or peer-reviewed research is expected. Defining and implementing rigorous evaluation protocols, developing CI/CD pipelines and containerized workflows for scalable ML solutions, and identifying risks in AI applications to contribute to responsible AI initiatives are also key duties.