SEAL Research Scientist, Frontier Risk Evaluations EPD San Francisco, CA

Scale AI, Inc.; San Francisco (CA); On-site; USD 176,000 - 300,000; Full time

Job summary

Join a forward-thinking company as a Research Scientist focused on Frontier Risk Evaluations, where you will play a crucial role in shaping the safety and transparency of advanced AI systems. This innovative firm is dedicated to tackling complex challenges in AI evaluation and alignment, offering you the opportunity to design and create evaluation measures that address the risks posed by cutting-edge AI technologies. With a commitment to collaborative research and a passion for advancing AI safety, you will work alongside talented peers to make a significant impact in the industry. If you're ready to contribute to a mission-driven environment that values diversity and inclusion, this is the perfect opportunity for you.

Benefits

Equity

Health Benefits

Flexible Working Hours

Remote Work Options

Professional Development Opportunities

Qualifications

• 3+ years of experience in ML research or product development.
• Published research in machine learning, especially generative AI.

Responsibilities

• Design evaluation measures for assessing risks of frontier AI systems.
• Collaborate with government agencies on AI risk evaluations.

Skills

Machine Learning

Research Collaboration

Communication Skills

Prototyping

Education

PhD in Machine Learning or related field

Master's in Data Science

Tools

Pytorch

Jax

TensorFlow

AWS

GCP

Job description

SEAL Research Scientist, Frontier Risk Evaluations

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 Alignment Lab (SEAL) is Scale’s frontier research effort dedicated to tackling the challenging research problems in evaluation, red teaming, and alignment of advanced AI systems.

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 academia and the publication of our research findings.

As a Research Scientist focused on Frontier Risk Evaluations, you will design and create evaluation measures, harnesses and datasets for measuring the risks posed by frontier AI systems. For example, you might do any or all of the following:

1. Design and build harnesses to test AI agents for dangerous capabilities such as hacking or exploiting security vulnerabilities;
2. Develop and run human-in-the-loop tests of AI capabilities to deceive, manipulate, blackmail, or otherwise engage in social engineering;
3. Work with government agencies or other labs to collectively scope and design evaluations to measure and mitigate risks posed by advanced AI systems.

Ideally you’d have:

1. Commitment to our mission of promoting safe, secure, and trustworthy AI deployments in the industry as frontier AI capabilities continue to advance.
2. 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.
3. A track record of published research in machine learning, particularly in generative AI.
4. At least three years of experience addressing sophisticated ML problems, whether in a research setting or in product development.
5. Strong written and verbal communication skills to operate in a cross functional team.

Nice to have:

1. Hands-on experience with open source LLM fine-tuning or involvement in bespoke LLM fine-tuning projects using Pytorch/Jax.
2. Experience in crafting evaluations or a background in data science roles related to LLM technologies.
3. 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.

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position, determined by work location and additional factors, including job-related skills, experience, interview performance, and relevant education or training.

The base salary range for this full-time position in the location of San Francisco is:

$176,000 — $300,000 USD

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, we believe that the transition from traditional software to AI is one of the most important shifts of our time. Our mission is to make that happen faster across every industry, and our team is transforming how organizations build and deploy AI. Our products power the world's most advanced LLMs, generative models, and computer vision models. We are trusted by generative AI companies such as OpenAI, Meta, and Microsoft, government agencies like the U.S. Army and U.S. Air Force, and enterprises including GM and Accenture. 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 [e-mail].