Alice is a leading platform for Trust & Safety teams worldwide, combining cutting-edge AI with world-class expertise to protect users from the widest spectrum of online harms and keep customers two steps ahead of bad actors.
We are looking for a Senior GenAI Data Scientist with strong hands-on experience in GenAI red teaming, adversarial testing, machine learning, and large-scale data analysis.
You will develop the data science and ML capabilities behind an automated GenAI red-teaming platform. You will analyze and cluster adversarial prompts, identify emerging attack patterns, build models for automated prompt generation and mutation, and develop data-driven methods for prioritizing and evaluating security risks across different AI models.
We are looking for a hands-on technologist with deep expertise in data science: clustering, scaling, machine learning, and statistical analysis.
Key Responsibilities
Build agents that act as data scientists at scale, reasoning, planning, and executing multi-step workflows to generate and analyze adversarial attacks.
Manage and analyze prompt and response data from multiple sources (red-team campaigns, model evaluations, synthetic attacks); clean, curate, normalize, and label it for analysis.
Analyze large volumes of structured and unstructured data to uncover trends, clusters, and anomalies, such as attack families, jailbreak techniques, and previously unseen patterns.
Develop ML models and predictive algorithms to automate red-teaming.
Build evaluation and monitoring pipelines to measure attack success and track model behavior across models and providers.
Use statistical techniques and experiments to validate findings and ensure accuracy and reproducibility.
Take research to production: build reliable, scalable systems optimized for cost and latency, with solid engineering practices (testing, CI/CD, telemetry).
Requirements: Must-Have
M.Sc. in CS/EE/Math/Statistics or a related field; Ph.D. is an advantage.
5+ years of hands-on data science or ML experience, programming in Python or R, and SQL.
3+ years of experience with Scikit-Learn and PyTorch.
Hands-on experience in GenAI red teaming, adversarial testing, or LLM security.
Hands-on experience building and deploying LLM-powered or ML systems in production.
Strong understanding of LLM design patterns, including prompt engineering, tool calling, structured outputs, and RAG.
Strong grasp of clustering, embeddings/NLP, semantic similarity, and anomaly/novelty detection.
Experience building classification, ranking, or prioritization systems.
Solid background in statistics, experimental design, and model evaluation.
Experience with Cradle, Apache Hadoop, and Spark, and with scaling ETL and data pipelines.
Nice-to-Have
Experience building LLM agents or multi-agent workflows with frameworks such as LangGraph, LangChain, or OpenAI Agents SDK.
Familiarity with evaluation frameworks, MLOps pipelines, and AI observability tooling.
Data visualization with Tableau, Power BI, or matplotlib.
Experience with AWS, including Lambda, S3, and EC2.
Experience running inference on GenAI models via Hugging Face, Bedrock, and Azure.
Background in Trust & Safety, security, or regulated environments.
This position is open to all candidates.