About the role
Conduct supervised fine-tuning of LLMs, applying Reinforcement Learning from Human Feedback in the process, and conducting thorough evaluation of those. Work with tech lead on the design and implementation of Agent adversarial evaluations and safety mitigations, enabling agent creators to identify potential risks and apply appropriate safeguards to ensure the safety and security of Agents. You will collaborate closely with the Responsible AI engineering team to deliver these mitigations at scale for Responsible AI customers, refine existing implementations based on operational performance, and troubleshoot and resolve end-to-end issues. Bachelor's Degree in Computer Science, Computational Linguistics, or related field AND 2+ years related experience (e.g., statistics, predictive analytics, research) OR Master's Degree in Computer Science, Computational Linguistics, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Computer Science, Computational Linguistics, or related field OR equivalent experience. These
requirements include but are not limited to the following specialized security screenings: Master's Degree in Computer Science, Computational Linguistics, related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Computer Science, Computational Linguistics, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. 2+ years of science experience owning feature design, model development, evaluation and deployment. 3+ years of science experience owning feature design, model development, evaluation and deployment. Full stack (client-to-service) development experience is a plus. Rich experience in machine learning, particularly in NLP and deep learning. Experience with language model training and evaluation is a big plus. Experience with research in Responsible AI is a big plus. Proficiency in programming languages such as Python, C#, and familiarity with machine learning frameworks including PyTorch and Triton. Experience with data processing and handling large datasets. Experience in engineering methodologies: Unit testing, Test Driven Development, DevOps, and a firm commitment to quality. Solid platform/API design, debugging and data analysis skills. Ability to work collaboratively in a team and communicate complex concepts effectively.
