Responsible AI Data Scientist

$125,700 - $188,600/Yr

Salesforce - San Francisco, CA

Full-time - Mid Level

San Francisco, CA

1,001-5,000 employees

Publishing Industries

About the position

The Responsible AI Data Scientist at Salesforce will focus on ensuring that machine learning models are developed and deployed in alignment with ethical guidelines and policies. This role involves collaborating with various teams to assess machine learning techniques, build datasets, and develop tools that promote fair, accountable, and transparent AI. The ideal candidate will provide technical leadership and support a culture of responsible AI development within the organization.

Responsibilities

• Partner with the Salesforce AI Research Science team and distributed Data Science and Engineering teams to measure and remediate potential bias in data and models.
• Build datasets for testing models, both generative and predictive, in collaboration with partner teams.
• Participate in technical discussions and drive engineering investments in responsible AI to improve model fairness and safety prior to launch.
• Develop quantitative metrics to help surface insights from structured datasets.
• Plan and conduct end-to-end experiments and analyses, from gathering requirements through processing and modeling the data, to identifying emerging risks.
• Assist in data curation to develop safety detectors.
• Validate any synthetic data generation, crowdsource, or contractor jobs for labeling or data creation.
• Participate in labeling test data in partnership with OEHU and partner teams.
• Collaborate with industry leaders in similar positions in peer organizations on ways to improve the state of responsible AI development.

Requirements

• Practical experience in machine learning.
• MS in a quantitative discipline with 8+ years of experience.
• Fluent in building/prototyping machine learning models and algorithms and wrangling large datasets.
• Knowledgeable about standard machine learning approaches (Regression, Cross-Validation, Boosting, Matrix-Factorization, Decision Trees, Clustering, CNNs, RNNs, Transformers, GANs).
• Proficient in using Python and common machine learning frameworks (e.g., TensorFlow, Pandas, PyTorch, SciPy, scikit-learn, JAX) to implement models and algorithms.
• Proficient in SQL, shell scripting, and Unix/Linux command-line tools.
• Grasp of the evolving understanding of fairness and ability to meet both state-of-the-art and global standards for fairness evaluation, particularly in generative AI.
• Experience working across teams of engineers, data scientists, and researchers.
• Strong communication skills.
• Comfortable presenting ideas to diverse teams and individuals in multiple formats, from slide decks to informal chats.
• Earns trust in relationships both internally and externally, and at all levels of the organization.
• Challenges the status quo to improve the productivity, effectiveness, and culture of a team - without burning bridges.
• Ability to creatively prioritize, stage, and sequence solutions to challenging/complex problems.

Nice-to-haves

• Demonstrated experience with bringing data science solutions from problem identification to production.
• Experience with designing and building micro-services, familiar with Kubernetes/containerization/RESTful API/gRPC, etc.
• Experience building and training various deep learning algorithms from scratch.
• Strong experience leading multi-disciplinary teams driving significant business results.
• Passion for the idea that technology can be a force for social good and for ethics and fairness.
• Ability to stay abreast of the latest findings in the field, new technologies, and best practices.
• Knowledge of enterprise SaaS space.
• Works well under pressure, and is comfortable working in a fast-paced, ever-changing environment.

Benefits

• Health insurance
• Dental insurance
• Vision insurance
• 401(k) plan
• Paid holidays
• Paid time off
• Flexible scheduling
• Professional development opportunities
• Employee resource groups
• Tuition reimbursement