Member of Technical Staff, Training

Key Responsibilities: Design, develop, and optimize architectures for diffusion-based language models. Implement innovative training objectives and loss functions for discrete diffusion LLMs. Research and implement techniques for controlled text generation and constraint satisfaction. Develop methods for multi-modal integration within the diffusion framework. Improve model efficiency, reduce training time, and optimize inference throughput. Develop and implement post-training techniques to align and improve model behavior.

Qualifications: 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).Familiarity with training and inference in diffusion models. Experience training deep learning models at scale in distributed computing environments.

Preferred Skills: Extensive experience training transformer-based language models from scratch. Knowledge of advanced training techniques (mixed precision, gradient accumulation, etc.).Experience with multi-modal learning and cross-modal architectures. Background in optimization theory and neural network architecture design. Experience with LLM serving frameworks like vLLM, SGLang, or TensorRT.