Research, Safety

Job summary

Responsibilities

As a safety researcher, you'll work toward ensuring our models are safe and trustworthy. The role sits at the intersection of research and hands-on technical work. A central question is how models come to handle harmful or dual-use requests: what they learn from data, how training shapes where…

• 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…
• 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.

What they're looking for

• Bachelor’s degree or equivalent experience in Computer Science, Machine Learning, Physics, Mathematics, or a related discipline with strong theoretical and empirical grounding.
• Background in AI safety research, with hands-on experience in at least one area of safety, such as: RLHF/RLAIF, alignment and preference modeling, deliberative alignment, safety…
• Proficiency in Python and familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow, or JAX). Comfort debugging distributed training and writing code that scales.
• Clarity in communication, an ability to explain complex technical concepts in writing.
• Experience building evaluations for long-horizon, multi-step, or agentic tasks.

Tech stack

• Python
• PyTorch
• TensorFlow
• JAX