Machine Learning Research Scientist / Research Engineer, Post-Training

<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). This role will focus on optimizing data curation and eval to enhance LLM capabilities in both text and multimodal modalities.</p> <p>In this role, you will develop novel methods to improve the alignment and generalization of large-scale generative models. You will collaborate with researchers and engineers to define best practices in data-driven AI development. You will also partner with top foundation model labs to provide both technical and strategic input on the development of the next generation of generative AI models.</p> <p><strong>You will:</strong></p> <ul> <li>Research and develop novel post-training techniques, including SFT, RLHF, and reward modeling, to enhance LLM core capabilities in both text and multimodal modalities.</li> <li>Design and experiment new approaches to preference optimization.</li> <li>Analyze model behavior, identify weaknesses, and propose solutions for bias mitigation and model robustness.</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.</li> <li>Excellent written and verbal communication skills</li> </ul> <ul> <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, determined by work location and additional factors, including job-related skills, experience, 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>$252,000</span><span class="divider"></span><span>$315,000 USD</span></div></div></div><div class="content-conclusion"><p><strong><em>PLEASE NOTE: </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, Cisco, DLA Piper, Mayo Clinic, ... (truncated, view full listing at source)