AI Research Scientist, Reinforcement Learning (LLM) and Post-Training
Santa Clara, CA$204k - $306kFull-timePosted 1mo ago
About the role
AI Research Scientist focused on reinforcement learning for post-training and interactive learning of large language/model-based systems, applied to demanding engineering and hardware-adjacent tasks (code, optimization, tool use, long-horizon decision making).
Responsibilities
- Research and develop RL methods for post-training LLMs and code models on structured engineering tasks with verifiable or preference-based feedback
- Design reward models, curricula, and off-policy or on-policy training recipes suited to sparse, noisy, or expensive labels from experts and simulators
- Characterize failure modes (reward hacking, degenerate policies, instability) and propose mitigations grounded in experiments
- Collaborate with RL infra engineers to scale training; define interfaces for rollout generation, logging, and reproducibility
- Publish at top venues (e.g. NeurIPS, ICML, ICLR) and contribute internal technical leadership on the RL roadmap
Qualifications
- Strong publication record in reinforcement learning or closely related machine learning areas
- Hands-on experience training RL or preference-optimized models at non-trivial scale (GPUs, distributed jobs)
- Experience with LLM post-training, RLHF/RLAIF, or policy optimization for language or code agents
- Familiarity with compilers, kernels, EDA-style workflows, or large-scale codebases is a plus
- PhD in Computer Science, Machine Learning, or related field strongly preferred
Skills
Required:Reinforcement LearningExperimentation & Evaluation
Nice to have:LLM Post-Training / RLHF / RLAIF
Last updated Oct 22, 2026. Interested? Apply on the company site.
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