As the leading data and evaluation partner for frontier AI companies, Scale plays an integral role in understanding the capabilities and safeguarding large language models (LLMs). Safety, Evaluations and Analysis Lab (SEAL) is Scale’s new frontier research effort dedicated to building robust evaluation products and tackling the challenging research problems in evaluation and red teaming.
We are actively seeking talented researchers to join us in shaping the landscape for safety and transparency for the entire AI industry. We support collaborations across the industry and the publication of our research findings. Below is a list of SEAL’s representative projects:
- Design and implement robust evaluation benchmarks. Measure and improve eval reproducibility and reliability.
- Research and implement cutting-edge model assisted red teaming methods.
- Develop state of the art LLM based automated rating systems leveraging the Scale ecosystem for customized training data.
- Develop rater assist techniques such as critique modeling for improving rating quality.
Ideally you’d have:
- Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry.
- Practical experience conducting technical research collaboratively, with proficiency in frameworks like Pytorch, Jax, or Tensorflow. You should also be adept at interpreting research literature and quickly turning new ideas into prototypes.
- 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:
- Hands-on experience with open source LLM fine-tuning or involvement in bespoke LLM fine-tuning projects using Pytorch/Jax.
- Experience in crafting evaluations or a background in data science roles related to LLM technologies.
- Experience working with cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environment.
Our research interviews are crafted to assess candidates' skills in practical ML prototyping and debugging, their grasp of research concepts, and their alignment with our organizational culture. We will not ask any LeetCode-style questions.
