About Pinterest: Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible. At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI. Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here. Pinterest is the world's leading visual search and discovery platform, serving over 500 million monthly active users globally on their journey from inspiration to action. As we scale Generative AI experiences — including Pinterest Assistant and Canvas — ensuring these products are safe, fair, and trustworthy is paramount. We are looking for a Senior Data Scientist to help lead Pinterest's responsible AI mandate within the Advanced Technology Group (ATG). In this role, you will design and build the data science foundations for automated adversarial testing of our GenAI products — developing attack strategies, evaluation frameworks, and harm-detection methodologies that operate at scale. You will work in a highly collaborative and cross-functional environment, partnering with ML Engineers, Trust & Safety specialists, Policy teams, and Product Managers. You are expected to develop a deep understanding of generative AI vulnerabilities, and generate insights and robust methodologies to proactively surface and mitigate risks. The results of your work will directly influence product safety, policy compliance, and user trust across Pinterest. What you'll do - Design and develop automated adversarial testing methodologies — including single-turn, multi-turn, and multimodal attack strategies — to proactively identify vulnerabilities in Pinterest's Generative AI products. - Build and calibrate hybrid evaluation pipelines combining LLM-based judges, classifiers, and rule-based systems to accurately detect safety violations, policy breaches, bias, and representational harms. - Develop and operationalize harm taxonomies grounded in industry standards and Pinterest's Responsible AI and Trust & Safety threat models. - Design adaptive refinement loops that learn from attack outcomes (near-misses, partial failures) to iteratively surface deeper and previously unknown vulnerabilities. - Bring scientific rigor and statistical methods to the evaluation of AI safety — including benchmark dataset construction, evaluation calibration, and success-metric definition (vulnerability severity, coverage breadth, pre-launch risk reduction). - Work cross-functionally to build relationships, proactively communicate key findings, and collaborate closely with ML engineers, Trust & Safety specialists, policy teams, product managers, and legal partners to ensure safe product launches. - Relentlessly focus on impact, whether through influencing product safety strategy, advancing responsible AI metrics, or improving critical evaluation processes. - Mentor and up-level junior data scientists and cross-functional partners on adversarial evaluation, responsible AI methodologies, and safety-aware data science practices. What we're looking for - 5+ years of experience analyzing data in a fast-paced, data-driven environment with proven ability to apply scientific methods to solve real-world problems on web-scale data. - Strong interest and hands-on experience in one or more of: AI safety, adversarial machine learning, red teaming, responsible AI, or trust & safety. - Deep familiarity with large language models (LLMs), generative AI systems, and their failure modes — including prompt injection, jailbreaks, bias, and safety violations. - Experience designing and calibrating evaluation frameworks for AI systems — including LLM-as-judge, classifier-based evaluation, and benchmark dataset construction. - Strong quantitative programming (Python) and data manipulation skills (SQL/Spark); experience with ML pipelines and large-scale experimentation. - Familiarity with AI safety taxonomies and frameworks (e.g., OWASP LLM Top 10, MITRE ATLAS) is strongly preferred. - Ability to work independently, drive ambiguous projects end-to-end, and operate with high ownership. - Excellent written and verbal communication skills, with the ability to explain complex technical findings to both technical and non-technical partners. - A team player eager to partner across Responsible AI, Trust & Safety, Product, Engineering, Policy, and Legal to turn safety insights into action. This position is not eligible for relocation assistance. #LI-NM4 At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise. Information regarding the culture at Pinterest and benefits available for this position can be found here. US based applicants only $139,764—$287,749 USD Our Commitment to Inclusion: Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, national origin, religion or religious creed, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, age, marital status, status as a protected veteran, physical or mental disability, medical condition, genetic information or characteristics (or those of a family member) or any other consideration made unlawful by applicable federal, state or local laws. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require a medical or religious accommodation during the job application process, please complete this form for support.

• Architect Fraud Solutions: Partner with product & engineering to design and implement high-performance, scalable AI/ML models to detect and prevent fraud in data-intensive applications. • Drive Innovation: Champion best practices and explore emerging technologies to enhance the fraud detection platform. • Mentor and Lead: Provide technical leadership through architectural reviews and mentorship, fostering a culture of automation and continuous learning. • Influence Strategy: Partner with product and engineering leadership to define the roadmap, balancing model scaling with reliability trade-offs. • Solve Complex Challenges: Act as the technical escalation point for the "hairiest" problems, from data quality issues to model performance bottlenecks. • Own the Lifecycle: Manage projects from design and development to deployment, monitoring, and post-mortem analysis. • Collaborate Cross-Functionally: Translate complex business requirements and research into robust, production-ready AI solutions alongside engineers and product managers.

• Deeply understand business needs and translate business needs to a technical project. • Identify appropriate data sources to use. Perform data aggregations and feature engineering as needed. • Write Python programming code to make visualizations, build and implement models. • Be flexible and open to innovative ideas and alternative ways of solving problems. • Present the results to non-tech partners and influence them to use predictive models. • Develop, validate, and deploy advanced machine learning models to predict business outcomes and inform strategic and operational decisions. • Build and evaluate predictive models used in production systems, including real-time scoring and model-driven product workflows. • Assess model impact via A/B testing and other experimental methods to optimize business outcomes. • Stay abreast of industry best practices and state-of-the-art methodology in data science.

• You will design, implement, and evaluate advanced natural language understanding (NLU) systems that interpret complex clinical text and support data-driven medical decision making • Collaborate closely with clinical domain experts, fellow data scientists, ML engineers, and product teams to bring research prototypes into production systems • Explore new modeling approaches, optimize training pipelines, and contribute to the long-term direction of Solventum’s deep learning portfolio • Scoping and designing NLP/NLU applications that support clinical understanding of medical documents and downstream healthcare workflows • Translating business and clinical requirements into mathematical and experimental criteria, ensuring models are measurable, reliable, and auditable • Processing, aligning, and enriching diverse structured and unstructured medical datasets for deep learning, LLMs, and agentic AI systems • Developing and evaluating deep learning models, including transformer-based architectures and neural architectures such as CNNs and attention mechanisms for capturing linguistic patterns in clinical text • Understanding and mitigating model weaknesses, including bias, drift, hallucination behavior, and robustness concerns • Designing experiments and error analyses that explain model behavior and enable clear communication with both technical and non-technical audiences • Deploying models into Solventum's cloud environment, partnering with ML engineering to ensure reliability, observability, and compliance within regulated healthcare systems • Staying current with emerging research in NLP/LLMs, evaluating new methods and identifying opportunities to advance Solventum’s deep learning initiatives