We are hiring across the entire development stack — from pre-training data curation to safety-focused fine-tuning, evaluations, and red-teaming. During project selection we’ll take into account your interests and experience alongside organizational needs. This flexible approach allows us to match talented safety researchers with the teams where they'll have the greatest impact and growth potential.
Here are example areas you may contribute to depending on your area of expertise and interest:
- Build data filtering pipelines and quality classifiers to shape what models learn from pre-training corpora, and study how those early interventions affect downstream safety behavior.
- Apply post-training techniques, including RL from human and AI feedback and policy-based reasoning approaches, to shape how models handle harmful, sensitive, and dual-use requests.
- Design, build, and maintain safety evaluations, with particular focus on measuring model behavior on long-horizon and agentic tasks.
- Generate and curate synthetic data to train and evaluate models on refusal boundaries and safety-relevant behaviors.
- Red-team our models and products to surface failure modes, jailbreaks, and emergent risks before deployment, and design mitigations for what you find.