Protection Scientist Engineer @ OpenAI
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About The Team
OpenAI’s mission is to ensure that general-purpose artificial intelligence benefits all of humanity. We believe that achieving our goal requires real world deployment and iterative learning.
About The Role
The Protection Scientist Engineer role is interdisciplinary, merging data science, machine learning, investigations, and policy/protocol development. In this position, you will design and build systems to proactively identify and enforce against abuses on OpenAI’s products. This includes implementing robust abuse monitoring for new and existing products and prototyping defense systems against high-risk harms. You will also respond to urgent escalations and work closely with product, policy, operations, and engineering teams.
Role Responsibilities
- Scope and implement abuse monitoring for new product launches.
- Enhance processes to maintain monitoring for existing products.
- Prototype and mature detection, review, and enforcement systems.
- Collaborate with cross-functional teams to understand risks and data needs.
What You Bring
- At least 4 years experience in technical analysis and detection using SQL and Python.
- Experience in trust and safety with a strong investigative mindset.
- Background in data engineering and machine learning principles.
- Basic software development skills for writing production code.
- Experience in scaling and automating processes, especially with language models.
Additional Information
This role is based in our London office and requires participation in an on-call rotation. Some investigations may involve sensitive content. OpenAI is an equal opportunity employer committed to diversity and inclusion.
Key skills/competency
- Data Science
- Machine Learning
- SQL
- Python
- Investigation
- Abuse Monitoring
- Data Engineering
- Automation
- Policy Development
- Cross-functional Collaboration
How to Get Hired at OpenAI
🎯 Tips for Getting Hired
- Research OpenAI's culture: Understand their mission and values.
- Customize your resume: Highlight data science and ML projects.
- Prepare technical skills: Brush up on SQL, Python, and data pipelines.
- Practice behavioral questions: Reflect on team collaboration experiences.