Machine Learning Research Scientist, Evaluations

About the company

Scale AI provides labeled training data and annotation for ML and LLMs across text, audio, images, video and lidar, combining software, human-in-the-loop annotators, APIs and dataset-management platform Nucleus.

About this role

<p>Scale works with the industry's leading AI labs to provide high quality data and accelerate progress in GenAI research. We are looking for Research Scientists and Research Engineers with expertise in LLM post-training (SFT, RLHF, reward modeling) and evaluation. This role is on the evaluation pod within the GenAI Research Organization and will focus on building benchmarks and diagnosing model failure modes in both text and multimodal modalities.</p> <p>In this role, you will develop rigorous evaluations and diagnostic methods that reveal where frontier models fail and why. You will collaborate with researchers and engineers to define best practices in evaluation-driven AI development. You will also partner with top foundation model labs to translate failure analysis into technical and strategic input on the next generation of generative AI models.</p> <p><strong>You will:</strong></p> <ul> <li>Analyze model behavior to identify, characterize, and diagnose failure modes in frontier LLMs and Agents.&nbsp; You’ll identify everything from capability gaps and reasoning errors to robustness and alignment issues, all focusing on RCA.</li> <li>Design and build benchmarks and evaluation methods that measure LLM capabilities in both text and multimodal modalities.</li> <li>Apply post-training expertise (SFT, RLHF, reward modeling) to connect observed failures to the data and training interventions that address them.</li> <li>Publish research findings in top-tier AI conferences.</li> </ul> <p><strong>Ideally you’d have:</strong></p> <ul> <li>Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or a related field.</li> <li>Deep understanding of deep learning, reinforcement learning, and large-scale model fine-tuning.</li> <li>Experience with post-training techniques such as RLHF, preference modeling, or instruction tuning, and with LLM evaluation or benchmark development.</li> <li>Excellent written and verbal communication skills.</li> <li>Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals.</li> <li>Previous experience in a customer facing role.</li> </ul><div class="content-pay-transparency"><div class="pay-input"><div class="description"><p><em>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 and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.</em></p></div><div class="title">Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is:</div><div class="pay-range"><span>$180,600</span><span class="divider">—</span><span>$225,750 USD</span></div></div></div><div class="content-conclusion"><p><strong><em>PLEASE NOTE:&nbsp;</em></strong><em>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.</em></p> <p><strong>About Us:</strong></p> <p><em>At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's 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, </em><em>Ernst</em><em> </em><em>&amp;</em><em> 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.</em></p> <p><em>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.&nbsp;</em></p> <p><em>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 accommodations@scale.com. Please see the United States Department of Labor's </em><a href="https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf"><em>Know Your Rights poster</em></a><em> for additional information.</em></p> <p><em>We comply with the United States Department of Labor's </em><em>Pay Transparency provision</em><em>.&nbsp;</em></p> <p><em><strong>PLEASE NOTE: </strong>We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and neces