At Scale, our mission is to accelerate the development of AI applications. We are working on an arsenal of proprietary research and resources that serve all of our enterprise clients. As an ML Sys Research Engineer, you'll work on building out the algorithms for our next-gen Agent RL training platform, support large scale training, and research and integrate state-of-the-art technologies to optimize our ML system.
\nYour customer will be other MLREs and AAIs on the Enterprise AI team who are taking the training algorithms and applying them to client use-cases ranging from next-generation AI cybersecurity firewall LLMs to training foundation healthtech search models.
\nIf you are excited about shaping the future of the modern AI movement, we would love to hear from you!
\nKey Responsibilities:
\nIdeal Candidate:
\nCompensation:
\nWe offer competitive compensation packages, including 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.
\nBenefits:
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\nYou 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 optimising the platform to enable our next generation LLM training, inference and data curation.
\nKey responsibilities include:
\nThe ideal candidate will have:
\nNice to haves include 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.
\nCompensation 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.
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\nScale's ML platform (RLXF) team builds our internal distributed framework for large language model training and inference. The platform has been powering MLEs, researchers, data scientists and operators for fast and automatic training and evaluation of LLM's, as well as evaluation of data quality.
\nAt Scale, we're 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 across Scale's ML teams and researchers to build the foundation platform that supports all our ML research and development. You will be building and optimizing the platform to enable our next generation of LLM training, inference and data curation.
\nResponsibilities:
\nIdeal Candidate:
\nNice to Have:
\nCompensation Packages:
\nCompensation 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.
\nPlease note that 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.
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\nNice to have:
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\nStrong candidates may also have experience with:
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