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Jobs · OTHR · California

Research Scientist - RL Training

Snorkel AI · San Francisco, CA · 1 wk ago
OTHRFull-time

Main Responsibilities

Research and implement reinforcement learning techniques — including GRPO, RLHF, RLAIF, DPO, and reward modeling — and translate them into data products (preference datasets, reward signals, verifiable rewards) that customers can use to train and fine-tune large language models.

Design and build data pipelines that generate high-quality training signal for RL workflows, including AI-assisted data annotation and curation data pipelines to improve model generalization to unseen benchmarks.

Prototype and iterate on end-to-end RL training recipes that inform what data Snorkel ships as part of its data-as-a-service deliveries.

Work closely with research scientists, ML engineers, and delivery teams to translate RL research into customer-ready data products.

Stay current with the latest developments in large-scale multi-node LLM training, alignment research, and scalable RL methods (on complex environments such as Terminal-Bench), bringing relevant advances into Snorkel's data-as-a-service approach.

Contribute to Snorkel's research publications and internal knowledge base in RL and model training.

PREFERRED QUALIFICATIONS

  • Deep expertise in reinforcement learning from human or AI feedback, reward modeling and credit attribution ideally with a clear perspective on what data makes these techniques work.
  • Experience training or fine-tuning 30B+ large language models at scale, including familiarity with distributed training infrastructure.
  • Strong proficiency in Python and ML frameworks, especially PyTorch and HuggingFace and hands-on experience with RL frameworks such as Verl and SkyRL.
  • Solid software engineering fundamentals — you can build research prototypes that others can run, extend, and integrate into data production workflows.
  • Familiarity with ML infrastructure and cloud platforms and tools (AWS, GCP, Kubernetes, Slurm, etc.); experience with large-scale RL training pipelines a strong plus.
  • Comfort operating in a high-iteration environment with open-ended research questions and shifting, customer-driven technical constraints.
  • Ph.D. in machine learning, reinforcement learning, or a related field strongly preferred; exceptional industry experience considered.

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