RE/RS, Data Understanding (MM)

About the Role

As part of the Data Understanding team, the RE/RS will collaborate closely with other researchers and engineers to drive improvements in data quality and model performance. This role is crucial for OpenAI's mission to safely deploy AI systems that benefit all of humanity, and the team's work has a direct impact on the development of more accurate and reliable AI models.

What You Will Do

• Develop and implement new methods for synthesizing multimodal data, including images, audio, and video.
• Improve the quality of noisy data pipelines by designing and deploying better filters and quality control measures.
• Use machine learning models to automate data preparation and processing tasks.
• Collaborate with the research team to design and conduct experiments that measure the impact of dataset changes on model performance.
• Work on the deduplication and tokenization of datasets to prepare them for use in large-scale model training.
• Develop and maintain tools and scripts for data processing, filtering, and quality control.
• Participate in the development of data-centric machine learning approaches to improve model performance.
• Engage in applied research to advance the state-of-the-art in multimodal learning and data understanding.
• Contribute to the development of high-performance deep learning and large-scale data processing systems.
• Work closely with cross-functional teams to ensure that data understanding capabilities align with company goals and objectives.

What We Are Looking For

• A strong track record of new or improved ML ideas, demonstrated through publications, projects, or applied research.
• Experience in owning and driving a research agenda, from identifying the right problems to seeing long-term projects through to impact.
• Excitement about OpenAI's empirical, collaborative approach to research and a willingness to work in a fast-paced, dynamic environment.
• Strong understanding of machine learning principles and practices, including data preprocessing, model training, and evaluation.
• Experience with multimodal learning, audio, vision, video, synthetic data, or data-centric ML.
• Ability to work collaboratively in a remote team environment and communicate complex ideas effectively.
• Familiarity with deep learning frameworks and large-scale data processing systems.
• Strong programming skills in languages such as Python, with the ability to develop and maintain large codebases.

Nice to Have

• Experience in building high-performance deep learning or large-scale data processing systems.
• Thoughtfulness about AI's impact, including considerations for privacy, provenance, and data quality.
• Familiarity with principles of data privacy and security, and how they apply to large-scale dataset development.
• Knowledge of ethical considerations in AI development, including bias mitigation and transparency.

Benefits and Perks

• The opportunity to work on cutting-edge AI research and development projects.
• Collaborative and dynamic work environment with a team of highly skilled professionals.
• Flexible and remote work arrangements, with the ability to work from anywhere.
• Access to the latest technologies and tools for AI development.
• Competitive compensation and benefits package, including health insurance, retirement savings, and paid time off.
• Professional development opportunities, including training, workshops, and conference attendance.
• Chance to contribute to the development of responsible and beneficial AI systems that can positively impact society.

• To stand out in your application, ensure your resume and cover letter clearly highlight your experience with machine learning, data preprocessing, and multimodal data handling.
• Prepare to discuss specific projects you've led or contributed to, especially those involving multimodal data synthesis, quality control, or the application of ML models for data automation.
• Familiarize yourself with OpenAI's research and mission to demonstrate your understanding of the company's goals and your potential fit within the team.
• Be ready to discuss your approach to collaborative research and how you handle feedback and criticism in a fast-paced, dynamic environment.
• Consider creating a portfolio that showcases your work in ML and data understanding, including any relevant code snippets or research papers you've authored or co-authored.
• When discussing your experience, emphasize instances where you've overcome challenges in data quality, model performance, or team collaboration, and how these experiences have prepared you for this role.
• Practice explaining complex technical concepts in simple terms, as this will be crucial for success in a role that involves collaboration across different disciplines.