AI is becoming vitally important in every function of our society. At Scale, our mission is to accelerate the development of AI applications. For 9 years, Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including generative AI, defense applications, and autonomous vehicles. With our recent investment from Meta, we are doubling down on building out state of the art post-training algorithms to reach the performance necessary for complex agents in enterprises around the world.
The Enterprise ML Research team works on the front lines of this AI revolution. 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. Your 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. If you are excited about shaping the future of the modern AI movement, we would love to hear from you!
You will:
- Build, profile and optimize our training and inference framework.
- Post-train state of the art models, developed both internally and from the community, to define stable post-training recipes for our enterprise engagements.
- Collaborate with ML teams to accelerate their research and development, and enable them to develop the next generation of models and data curation..
- Create a next-gen agent training algorithm for multi-agent/multi-tool rollouts.
Ideally you’d have:
- At least 1-3 years of LLM training in a production environment
- Passionate about system optimization
- Experience with post-training methods like RLHF/RLVR and related algorithms like PPO/GRPO etc.
- Ability to demonstrate know-how on how to operate the architecture of the modern GPU cluster
- Experience with multi-node LLM training and inference
- Strong software engineering skills, proficient in frameworks and tools such as CUDA, Pytorch, transformers, flash attention, etc.
- Strong written and verbal communication skills to operate in a cross functional team environment.
- PhD or Masters in Computer Science or a related field
