Member of Technical Staff, Reinforcement Learning

Location: Bay Area

Department: Research

Location Type: IN_OFFICE

Employment Type: FULL_TIME

• Design, develop, and optimize RL training pipelines (PPO, DPO, RLHF, and novel approaches) for diffusion-based LLMs.
• Build and iterate on reward models, reward shaping strategies, and evaluation of reward quality.
• Implement innovative approaches for fine-tuning and scaling generative AI models.
• Work on data preprocessing pipelines, model evaluation, and alignment to enterprise use cases.
• Research and implement techniques for controlled text generation and constraint satisfaction.
• Improve training stability, efficiency, and reproducibility of RL workloads.

• BS/MS/PhD in Computer Science or a related field (or equivalent experience).
• At least 2 years of experience working on ML projects in PyTorch (or equivalent), preferably in a research lab or engineering role.
• Excellent familiarity with transformers and core LLM concepts (autoregressive pretraining, instruction tuning, in-context learning, KV caching).
• Hands-on experience with reinforcement learning from human feedback (RLHF), PPO, DPO, or related post-training methods.
• Familiarity with training and inference in diffusion models.
• Experience training deep learning models at scale in distributed computing environments.

• Extensive experience training transformer-based language models from scratch.
• Experience designing and implementing reward models or preference learning systems.
• Knowledge of advanced training techniques (mixed precision, gradient accumulation, etc.).
• Background in optimization theory and neural network architecture design.
• Experience with LLM serving frameworks like vLLM, SGLang, or TensorRT.