Research Staff, LLMs

Conteúdo da vaga extraído em seções para revisão mais rápida.

See "unsolved" problems as opportunities to pioneer entirely new approaches
Can identify the one critical experiment that will validate or kill an idea in days, not months
Have the vision to scale successful proofs-of-concept 100x
Are obsessed with using AI to automate and amplify your own impact

Brainstorming and collaborating with other members of the Research Staff to define new LLM research initiatives
Broad surveying of literature, evaluating, classifying, and distilling current methods
Designing and carrying out experimental programs for LLMs
Driving transformer (LLM) training jobs successfully on distributed compute infrastructure and deploying new models into production
Documenting and presenting results and complex technical concepts clearly for a target audience
Staying up to date with the latest advances in deep learning and LLMs, with a particular eye towards their implications and applications within our products

Are passionate about AI and excited about working on state of the art LLM research
Have an interest in producing and applying new science to help us develop and deploy large language models
Enjoy building from the ground up and love to create new systems.
Have strong communication skills and are able to translate complex concepts clearly
Are highly analytical and enjoy delving into detailed analyses when necessary

3+ years of experience in applied deep learning research, with a solid understanding toward the applications and implications of different neural network types, architectures, and loss mechanism
Proven experience working with large language models (LLMs) - including experience with data curation, distributed large-scale training, optimization of transformer architecture, and RL Learning
Strong experience coding in Python and working with Pytorch
Experience with various transformer architectures (auto-regressive, sequence-to-sequence.etc)
Experience with distributed computing and large-scale data processing
Prior experience in conducting experimental programs and using results to optimize models

Deep understanding of transformers, causal LMs, and their underlying architecture
Understanding of distributed training and distributed inference schemes for LLMs
Familiarity with RLHF labeling and training pipelines
Up-to-date knowledge of recent LLM techniques and developments

See "unsolved" problems as opportunities to pioneer entirely new approaches
Can identify the one critical experiment that will validate or kill an idea in days, not months
Have the vision to scale successful proofs-of-concept 100x
Are obsessed with using AI to automate and amplify your own impact

Brainstorming and collaborating with other members of the Research Staff to define new LLM research initiatives
Broad surveying of literature, evaluating, classifying, and distilling current methods
Designing and carrying out experimental programs for LLMs
Driving transformer (LLM) training jobs successfully on distributed compute infrastructure and deploying new models into production
Documenting and presenting results and complex technical concepts clearly for a target audience
Staying up to date with the latest advances in deep learning and LLMs, with a particular eye towards their implications and applications within our products

Are passionate about AI and excited about working on state of the art LLM research
Have an interest in producing and applying new science to help us develop and deploy large language models
Enjoy building from the ground up and love to create new systems.
Have strong communication skills and are able to translate complex concepts clearly
Are highly analytical and enjoy delving into detailed analyses when necessary

3+ years of experience in applied deep learning research, with a solid understanding toward the applications and implications of different neural network types, architectures, and loss mechanism
Proven experience working with large language models (LLMs) - including experience with data curation, distributed large-scale training, optimization of transformer architecture, and RL Learning
Strong experience coding in Python and working with Pytorch
Experience with various transformer architectures (auto-regressive, sequence-to-sequence.etc)
Experience with distributed computing and large-scale data processing
Prior experience in conducting experimental programs and using results to optimize models

Deep understanding of transformers, causal LMs, and their underlying architecture
Understanding of distributed training and distributed inference schemes for LLMs
Familiarity with RLHF labeling and training pipelines
Up-to-date knowledge of recent LLM techniques and developments