Member of Technical Staff, Training

Inception; San Francisco (CA); On-site; USD 180,000 - 230,000; Full time

Job summary

Inception seeks experienced scientists and engineers with deep expertise in pre-, mid-, and post-training large language models. You will advance diffusion-based LLM models, developing novel training techniques and pushing the boundaries of parallel token generation.

Key responsibilities include designing architectures for diffusion-based language models, implementing training objectives, and researching techniques for controlled text generation and multi-modal integration.

Qualifications

• BS/MS/PhD in CS (or equivalent).
• 2+ years ML experience in PyTorch, research or engineering setting.
• Excellent familiarity with transformers and core LLM concepts.
• Familiarity with training and inference in diffusion models.

Responsibilities

• Design, develop, and optimize architectures for diffusion-based language models.
• Implement training objectives and loss functions for discrete diffusion LLMs.
• Research 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 post-training techniques to align and improve model behavior.

Skills

PyTorch

Transformers

Distributed computing

LLM concepts

Diffusion models

Education

BS/MS/PhD in CS

Tools

vLLM

TensorRT

SGLang

Job description

The Role

We seek experienced scientists and engineers with deep expertise in pre-, mid-, and post- training large language models. You will advance our diffusion-based LLM models, developing novel training techniques and pushing the boundaries of parallel token generation.

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.