Senior Applied Scientist, AI Security

About the Role

Job Description

Our mission is to make Uber the industry model for a secure and trustworthy AI ecosystem through differentiated, highly scalable, and extensible products, engineering standards, policy, and open communications.

We are focusing on building robust defense mechanisms that protect the entire AI lifecycle-from internal infrastructure to external user-facing products. This includes securing the technology platform to protect model integrity, prevent data leakage, and enable safe business velocity.

Lead efforts within the organization to drive the development of secure AI/ML-based solutions in support of user-facing products, internal downstream services, or infrastructure tools and platforms used across Uber.

About the Team

Our mission is to make Uber the industry model for AI Safety and Security through differentiated products, engineering standards, and policy. We focus on a defense-in-depth approach, building protections that span from internal developer tools to external customer agents, ensuring that our AI systems remain resilient against adversarial attacks while preserving user trust.

About the Role

Lead efforts to develop and evaluate security controls for large-scale machine learning models, harden retrieval-augmented generation (RAG) systems, alignment-tune large language models (LLMs), and secure agentic workflows across internal and external boundaries. This role requires a strong foundation in both traditional machine learning, advanced LLM technologies, and adversarial machine learning.

What the Candidate Will Need / Bonus Points

\\-\\-\\-\\- What the Candidate Will Do ----

1. Develop and evaluate large-scale machine learning model systems in production with a focus on adversarial robustness, input validation, and output sanitization.
2. Propose, design, and analyze large-scale online experiments to test safety guardrails and detect ecosystem vulnerabilities.
3. Define and implement metrics to measure security posture, attack surface reduction, and product performance.
4. Present findings on AI risks, red-teaming results, and mitigations to business and executive audiences.
5. Collaborate with engineers and product managers to implement secure-by-design ideas and plan future roadmaps.
6. Optimize and secure retrieval-augmented generation (RAG) systems against prompt injection, indirect injection, and data exfiltration.
7. Fine-tune large language models (LLMs) to improve safety alignment, resistance to jailbreaking, and operational efficiency.
8. Implement secure agentic workflows to streamline processes, ensuring safe tool-use and authorization for both internal employee agents and external user agents.

\\-\\-\\-\\- Basic Qualifications ----

1. Ph.D., MS or Bachelors degree in Statistics, Economics, Operations Research, Computer Science, Engineering, or other quantitative field.
2. If Ph.D or M.S. degree, a minimum of 2+ years of industry experience as an Applied Scientist or equivalent.
3. Knowledge of underlying mathematical foundations of machine learning, statistics, optimization, economics, and analytics.
4. Hands-on experience building and deploying ML models.
5. Knowledge of experimental design and analysis.
6. Experience with exploratory data analysis, statistical analysis and testing, and model development.
7. Ability to use a language like Python or R to work efficiently at scale with large data sets.
8. Proficiency in technologies in one or more of the following: SQL, Spark, Hadoop.

\\-\\-\\-\\- Preferred Qualifications ----

1. Knowledge in modern machine learning techniques applicable to AI Security, Adversarial ML (AML), and model robustness.
2. Advanced understanding of statistics, causal inference, and machine learning.
3. 5+ years of industry experience as an Applied Scientist or equivalent.
4. Experience designing and analyzing large scale online experiments.
5. Experience working with large scale data sets using technologies like Hive, Presto, and Spark.
6. Experience with synthetic data generation for red-teaming and adversarial training.
7. Proficiency in fine-tuning and optimizing large language models (LLMs) for safety (e.g., RLHF, DPO).
8. Experience in securing retrieval-augmented generation (RAG) systems.
9. Familiarity with secure agentic workflows, sandboxing, and their applications in internal and external AI systems.

For Sunnyvale, CA-based roles: The base salary range for this role is USD$190,000 per year - USD$211,000 per year.

You will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following link [](;br>
Uber's mission is to reimagine the way the world moves for the better. Here, bold ideas create real-world impact, challenges drive growth, and speed fuels progress. What moves us, moves the world - let's move it forward, together.

Uber is proud to be an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing [this form](;br>
Offices continue to be central to collaboration and Uber's cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.