Tech Lead Manager- MLRE, ML Systems

<p>Scale's LLM post-training platform team builds our internal distributed framework for large language model training. The platform powers MLEs, researchers, data scientists, and operators for fast and automatic training and evaluation of LLMs. It also serves as the underlying training framework for the data quality evaluation pipeline.</p> <p>Scale is uniquely positioned at the heart of the field of AI as an indispensable provider of training and evaluation data and end-to-end solutions for the ML lifecycle. You will work closely with Scale’s ML teams and researchers to build the foundation platform which supports all our ML research and development works. You will be building and optimizing the platform to enable our next generation LLM training, inference and data curation.</p> <p>If you are excited about shaping the future AI via fundamental innovations, we would love to hear from you!</p> <h3>You will:</h3> <ul> <li>Build, profile and optimize our training and inference framework.</li> <li>Collaborate with ML and research teams to accelerate their research and development, and enable them to develop the next generation of models and data curation.</li> <li>Research and integrate state-of-the-art technologies to optimize our ML system.</li> </ul> <h3>Ideally you’d have:</h3> <ul> <li>Passionate about system optimization</li> <li>Experience with multi-node LLM training and inference</li> <li>Experience with developing large-scale distributed ML systems</li> <li>Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc.</li> <li>Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etc. </li> <li>Strong written and verbal communication skills to operate in a cross functional team environment.</li> </ul> <h3>Nice to haves:</h3> <ul> <li>Demonstrated expertise in post-training methods and/or next generation use cases for large language models including instruction tuning, RLHF, tool use, reasoning, agents, and multimodal, etc.</li> </ul> <p> </p><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 ... (truncated, view full listing at source)