Research Scientist, Safety Post Training

As the leading data and evaluation partner for frontier AI companies Scale plays an integral role in understanding the capabilities and safeguarding AI models and systems. Building on this expertise Scale Labs has launched a new team focused on policy research to bridge the gap between AI research and global policymakers to make informed scientific decisions about AI risks and capabilities.

Our research tackles the hardest problems in agent robustness AI control protocols and AI risk evaluations to help governments industry and the public understand and mitigate AI risk while maximizing AI adoption. This team collaborates broadly across industry the public sector and academia and regularly publishes our findings. We are actively seeking talented researchers to join us in shaping this vision.

As a Research Scientist working on Safety Post-Training you will develop and apply post-training methods and interpretability techniques to make frontier AI systems safer and better understood by researchers and policymakers.. For example you might:

• Design and run post-training pipelines to study how training choices affect model safety robustness and alignment properties;
• Develop interpretability-informed evaluations that reveal how and why models produce unsafe deceptive or otherwise undesirable behaviors and use those insights to guide targeted mitigations;
• Collaborate with policymakers engineers and other researchers to translate post-training and interpretability findings into actionable safety standards evaluation benchmarks and best practices.

Ideally youd have:

• Commitment to our mission of promoting safe secure and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance.
• Experience with post-training and RL techniques such as RLHF DPO GRPO and similar approaches.
• A track record of published research in machine learning particularly in generative AI.
• At least three years of experience addressing sophisticated ML problems whether in a research setting or in product development.
• Strong written and verbal communication skills to operate in a cross-functional team.

Nice to have:

• Experience with mechanistic interpretability probing or other techniques for understanding model internals.
• Familiarity with red-teaming or adversarial evaluation of post-trained models.
• Experience studying failure modes introduced or masked by post-training such as reward hacking sycophancy or alignment faking.

PLEASE NOTE:Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.

About Us:

At Scale our mission is to develop reliable AI systems for the worlds most important decisions. Our products provide the high-quality data and full-stack technologies that power the worlds leading models and help enterprises and governments build deploy and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta Ernst & Young Mayo Clinic Time Inc. the Government of Qatar and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications.

We believe that everyone should be able to bring their whole selves to work which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race color ancestry religion sex national origin sexual orientation age citizenship marital status disability status gender identity or Veteran status.

We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability please contact us at Please see the United States Department of Labors Know Your Rights poster for additional information.

We comply with the United States Department of Labors Pay Transparency provision.

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