Senior Applied Scientist – AI Red Teaming & Model Risk

Uber

Hybrid

Quick summary

Work type
Hybrid
Location
New York, NY · Seattle, WA · San Francisco, CA · Sunnyvale, CA
Salary
$190,000–$190,000 / yr
Posted
110 days ago

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $210k
This role $190k
$164k most similar roles pay here $272k

This role pays less than 70% of similar roles. Most pay $174,225–$246,150 — the shaded band above. At the midpoint, this role pays about $190k versus about $210k for comparable roles.

Based on 240 similar postings.

Employer

About Uber

Uber Technologies, Inc. is the world’s largest, San Francisco-based mobile technology platform facilitating on-demand ride-hailing, food delivery (Uber Eats), and freight transportation across approximately 70 countries.

Uber currently has 95 open roles on FindRole.

Listed pay typically runs $232,000–$232,000 across 76 roles with salary data.

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At a glance

TL;DR · Senior Applied Scientist – AI Red Teaming & Model Risk

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As a Senior Applied Scientist in AI Red Teaming and Model Risk, you will join a specialized team focused on ensuring the safety and robustness of advanced AI systems, particularly large language models (LLMs) and agentic AI. Your primary responsibilities include designing and executing adversarial experiments to identify potential risks such as prompt injection, jailbreaking, and behavioral drift, while also developing evaluation frameworks and metrics that go beyond traditional accuracy measures. You will collaborate closely with security engineers and platform teams to translate your findings into actionable improvements for real-world AI systems. Ideal candidates have extensive experience in data science, applied machine learning, and a deep understanding of AI red teaming techniques. Proficiency in Python and familiarity with LLMs and generative AI architectures are essential, along with expertise in experimental design and statistical analysis. Additional knowledge in evaluating agentic systems, building custom evaluation datasets, and an interest in security and privacy is highly beneficial as you work to mitigate risks in cutting-edge AI technologies at scale.

Skills

Python LLMs AI red teaming prompt injection jailbreaking model safety adversarial evaluation statistical analysis transformers embeddings RAG agent frameworks custom evaluation datasets simulation environments security privacy trust and safety ML metrics evaluation pipelines

What you'll do

  • Design and execute AI red-teaming experiments to identify various failure modes in LLMs.
  • Develop adversarial datasets and test harnesses for systematic evaluation of model behavior under attack.
  • Define and track risk metrics beyond accuracy to capture complex failure scenarios.
  • Analyze agent workflows and decision traces to understand multi-step reasoning failures.
  • Build reusable evaluation pipelines for continuous red teaming as models evolve.

What we're looking for

  • 5+ years of experience as a Data Scientist, Applied Scientist, or ML Scientist.
  • Hands-on experience with LLMs and generative AI systems.
  • Direct experience in AI red teaming, model safety, and adversarial evaluation.
  • Expertise in designing experiments to evaluate complex model behavior and failure cases.
  • Proficiency in Python and common data science/machine learning tools.

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