Machine Learning Systems Research Engineer, Agent Post-training - Enterprise GenAI
Develops and optimizes post-training algorithms for agent RL platforms, focusing on LLM training, inference frameworks, and multi-agent systems. Requires 1-3 years production LLM experience, expertise in PyTorch/CUDA, RLHF/PPO, and advanced degree.
About the job
Responsibilities
- 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.
Requirements
- 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
Skills
PyTorch, CUDA, Transformers, Flash Attention, RLHF, Rlvr, Ppo, Grpo, Gpu Cluster, Multi-Node Training
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