Member of Technical Staff - Safety

• Own the red-teaming and adversarial evaluation pipeline for Reflection’s models, continuously probing for failure modes across security, misuse, and alignment gaps.
• Work hand-in-hand with the Alignment team to translate safety findings into concrete guardrails, ensuring models behave reliably under stress and adhere to deployment policies.
• Validate that every release meets the lab’s risk thresholds before it ships, serving as a critical gatekeeper for our open weight releases.
• Develop scalable, automated safety benchmarks that evolve alongside our model capabilities, moving beyond static datasets to dynamic adversarial testing.
• Research and implement state-of-the-art jailbreaking techniques and defenses to stay ahead of potential vulnerabilities in the wild.
• Graduate degree (MS or PhD) in Computer Science, Machine Learning, or related discipline, or equivalent practical experience in AI Safety.
• Deep technical understanding of LLM safety, including adversarial attacks, red-teaming methodologies, and interpretability.
• Strong software engineering capabilities with experience building automated evaluation pipelines or large-scale ML systems.
• Experience with Reinforcement Learning (RLHF/RLAIF) and how it impacts model safety and alignment is a strong plus.
• Thrive in a fast-paced, high-agency startup environment with bias toward action.
• Willing to make high-stakes decisions regarding model release and safety thresholds.
• Passionate about advancing the frontier of intelligence.

None flagged.