Senior Staff ML Engineer - AI Safety & Evaluation

About the Team
We’re building a future where AI systems are not only powerful but safe, aligned, and robust against misuse. Our team focuses on advancing practical safety techniques for large language models (LLMs) and multimodal systems—ensuring these models remain aligned with human intent and resist attempts to produce harmful, toxic, or policy-violating content.

We operate at the intersection of model development and real-world deployment, with a mission to build systems that can proactively detect and prevent jailbreaks, toxic behaviors, and other forms of misuse. Our work blends applied research, systems engineering, and evaluation design to ensure safety is built into our models at every layer.

 

About the Role
We’re looking for a Senior Staff Engineer to help lead our efforts in designing, building, and evaluating next-generation safety mechanisms for foundation models. You’ll guide a team of research engineers focused on scaling safety interventions, building tooling for red teaming and model inspection, and designing robust evaluations that stress-test models in realistic threat scenarios.

 

What You’ll Do

Lead the development of model-level safety defenses to mitigate jailbreaks, prompt injection, and other forms of unsafe or non-compliant outputsDesign and develop evaluation pipelines to detect edge cases, regressions, and emerging vulnerabilities in LLM behaviorContribute to the design and execution of adversarial testing and red teaming workflows to identify model safety gapsSupport fine-tuning workflows, pre/post-processing logic, and filtering techniques to enforce safety across deployed modelsWork with red teamers and researchers to turn emerging threats into testable evaluation cases and measurable risk indicatorsStay current on LLM safety research, jailbreak tactics, and adversarial prompting trends, and help translate those into practical defenses for real-world products 

 

What We’re Looking For

5+ years of experience in machine learning or AI systems, with 2+ years in a technical leadership capacityExperience integrating safety interventions into ML deployment workflows (e.g., inference servers, filtering layers, etc.)Good understanding of transformer-based models and experience with LLM safety, robustness, or interpretabilityStrong background in evaluating model behavior, especially in adversarial or edge-case scenariosStrong communication skills and ability to drive alignment across diverse teamsBachelor’s, Master’s, or PhD in Computer Science, Machine Learning, or a related field
 

Compensation up to 192K