Senior or Principal Data Scientist - Technical AI Ethicist

To get the best candidate experience, please consider applying for a maximum of 3 roles within 12 months to ensure you are not duplicating efforts.

Job Category

Data

Job Details

About Salesforce

We’re Salesforce, the Customer Company, inspiring the future of business with AI+ Data +CRM. Leading with our core values, we help companies across every industry blaze new trails and connect with customers in a whole new way. And, we empower you to be a Trailblazer, too — driving your performance and career growth, charting new paths, and improving the state of the world. If you believe in business as the greatest platform for change and in companies doing well and doing good – you’ve come to the right place.

Salesforce’s Office of Ethical and Humane Use is seeking an experienced responsible AI data scientist with an adversarial approach and experience conducting ethical red teaming to contribute to our ethical red teaming practice. In this role, you will help us gain a deep understanding of how our models and products may be leveraged by malign actors or through unanticipated use to cause harm. In addition to adversarial testing, you will analyze current safety trends, and develop solutions to detect and mitigate risk, while working cross-functionally with security, engineering, data science, and AI Research teams. You will bring technical depth to the assessment of AI products, models, and applications, in order to identify the best technical mitigations to identified risks.

The ideal candidate will have technical experience in generative as well as predictive artificial intelligence and in responsible / ethical AI
 

Responsibilities:

• Adversarial Testing

  • Provide technical leadership in designing, prototyping, and implementing comprehensive adversarial testing strategies, including both automated and manual adversarial testing approaches

  • Mentor and guide collaborator teams on adversarial testing standard processes, helping them develop the skills to conduct their own testing effectively

  • Collaborate with cross-functional teams to integrate OEHU adversarial testing frameworks into the AI development lifecycle
     

• Safety and Robustness

  • Contribute to the development of detection models, safety guardrails, and other proactive measures to prevent and mitigate risks posed by bad actors

  • Research and implement innovative techniques for enhancing AI safety and robustness, drawing from both open-source and internal tools

  • Collaborate with Salesforce’s AI Research team on novel approaches to model safety
     

• Technical Research and Implementation

  • Write clean, efficient, and well-documented code (primarily in Python) to support research efforts and facilitate the evaluation of AI systems

  • Develop and maintain a repository of reusable code modules and libraries to streamline adversarial testing processes
     

• Testing Execution and Collaboration

  • Participate in scoping, documenting, and implementing tests with partner teams, including the implementation of mitigations identified during testing

  • Test for technical vulnerabilities, model vulnerabilities, and harm/abuse including but not limited to bias, toxicity, and inaccuracy

  • Participate in labeling test data in partnership with OEHU and partner teams
     

• Reporting, Documentation, and Continuous Learning

  • Write reports covering the goals and outcomes of testing operations, including significant observations and recommendations

  • Continuously monitor and analyze emerging threats and vulnerabilities to inform the development of adaptive safety measures

  • Continue to grow expertise in model safety by keeping up with research in socio-technical systems, privacy, interpretability/explainability, robustness, alignment, and responsible AI

Qualifications:

• A related technical degree required

• 7+ years of proven experience in Software Engineering, AI ethics, AI research, or similar roles

• Experience creating heuristic-based detection logic and rules for identifying anomalous or suspicious activity in systems and networks  (e.g. log analysis, user behavior analytics)