About the Team

The Safety Systems team is responsible for various safety work to ensure our best models can be safely deployed to the real world to benefit the society. The work encompasses a wide range of research and engineering projects from detection to model training to model evaluation and red-teaming, aiming to reduce unwanted use cases and ensure model behavior within our safety standard and legal compliance. The Safety Systems team is at the forefront of OpenAI's mission to build and deploy safe AGI, driving our commitment to AI safety and fostering a culture of trust and transparency.

We seek to learn from deployment and distribute the benefits of AI, while ensuring that this powerful tool is used responsibly and safely. Safety is more important to us than unfettered growth.

About the Role

To help API users monitor and prevent unwanted use cases, we developed the moderation endpoint, a tool for checking whether content complies with OpenAI's content policy. Developers can thus identify content that our content policy prohibits and take actions (e.g. block it). We seek a Machine Learning Engineer to help design and build a robust pipeline for data management, model training and deployment to enable a consistent improvement on the Moderation model.

In this role, you will:

• Design, develop and maintain a robust and scalable data management pipeline and set up standards for versioning and data quality control. The pipeline should be able to handle data relabeling requests due to content policy changes.
• Build a pipeline for automated model training, evaluation and deployment, including active learning process, routines for calibration and validation data refresh etc.
• Work closely with stakeholders from product, engineering, content policy on a long-term improvement over the moderation models, for both external release and internal use cases across a variety of projects on model safety.
• Research on the latest techniques and methods in deep learning and natural language processing to improve the moderation model across a collection of unwanted content categories.
• Experiment on data augmentation and data generation methods to enhance the diversity and quality of training data.
• Experiment and design an effective red-teaming pipeline to examine the robustness of the model and identify areas for future improvement.
• Conduct open-ended research to improve the quality of collected data, including but not limited to, semi-supervised learning and human-in-the-loop machine learning.

You might thrive in this role if you:

• Have 3+ years industry experience as a Machine Learning Engineer or Software Engineer, working on building data pipelines, training and deploying machine learning models in production on a daily basis.
• Care deeply about AI safety and passionate about building the best deep learning empowered moderation model to effectively detect unwanted content.
• Have a strong belief in the criticality of high-quality data and are highly motivated to work with the associated challenges.
• Have experience working in large distributed systems, deep learning or/and natural language processing is a big plus.
• Love working with a team.

Compensation, Benefits and Perks

The annual salary range for this role is $200,000 – $370,000. Total compensation also includes generous equity and benefits.

• Medical, dental, and vision insurance for you and your family
• Mental health and wellness support
• 401(k) plan with 4% matching
• Unlimited time off and 18+ company holidays per year
• Paid parental leave (20 weeks) and family-planning support
• Annual learning & development stipend ($1,500 per year)

We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or any other legally protected status. Pursuant to the San Francisco Fair Chance Ordinance, we will consider qualified applicants with arrest and conviction records.

We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.