Machine Learning Specialist

As a Machine Learning Engineer Data Quality Lead, you will play a crucial role in ensuring the quality and reliability of training datasets for Large Language Models (LLMs). Your responsibilities will include: - Automating pipelines to guarantee the reliability, safety, and relevance of training datasets. - Conducting proxy fine-tuning and reward modeling experiments to validate data quality. - Developing evaluation frameworks that combine human judgment with LLM assessments to measure consistency and alignment. - Collaborating closely with annotation teams to implement scalable and high-quality data workflows. To excel in this role, you should possess the following qualifications: - Deep understanding of Large Language Model (LLM) training and evaluation techniques such as SFT, RLHF, and reward models. - Proficiency in Python, PyTorch, Hugging Face, and other evaluation tooling. - Ability to translate model diagnostics into actionable insights. - Self-driven individual with a consultative approach, comfortable navigating the intersection of research and client success. If you join our team, you will have the opportunity to: - Contribute to mission-driven AI projects that are at the forefront of enhancing model reliability. - Influence data pipelines that directly impact the development of next-generation AI systems. - Be part of a rapidly growing AI-focused team with a global footprint. If you are enthusiastic about data-centric AI and eager to work in an environment where data quality is synonymous with model excellence, we would love to discuss further with you. As a Machine Learning Engineer Data Quality Lead, you will play a crucial role in ensuring the quality and reliability of training datasets for Large Language Models (LLMs). Your responsibilities will include: - Automating pipelines to guarantee the reliability, safety, and relevance of training datasets. - Conducting proxy fine-tuning and reward modeling experiments to validate data quality. - Developing evaluation frameworks that combine human judgment with LLM assessments to measure consistency and alignment. - Collaborating closely with annotation teams to implement scalable and high-quality data workflows. To excel in this role, you should possess the following qualifications: - Deep understanding of Large Language Model (LLM) training and evaluation techniques such as SFT, RLHF, and reward models. - Proficiency in Python, PyTorch, Hugging Face, and other evaluation tooling. - Ability to translate model diagnostics into actionable insights. - Self-driven individual with a consultative approach, comfortable navigating the intersection of research and client success. If you join our team, you will have the opportunity to: - Contribute to mission-driven AI projects that are at the forefront of enhancing model reliability. - Influence data pipelines that directly impact the development of next-generation AI systems. - Be part of a rapidly growing AI-focused team with a global footprint. If you are enthusiastic about data-centric AI and eager to work in an environment where data quality is synonymous with model excellence, we would love to discuss further with you.