Code Metal
Our mission is to enable hardware deployment at the speed of software development. We are working towards automatic code transpilation and optimization for any hardware application.
In this role, you will collaborate with a small team of talented researchers on ambitious, greenfield projects in generative AI and reinforcement learning.
Core responsibilities:
Design, execute, and analyze experiments with a high degree of independence
Contribute to core models and frameworks
Create high-quality datasets (both in-the-wild and synthetic)
Perform literature reviews and implement new techniques from papers
Contribute to publications, present at conferences and workshops, etc.
Research Areas of Interest: An incomplete list of current and near-term research directions:
Contrastive representation learning
Steerability and guided decoding
Tractable probability models
Code-specific architectures
LLM fine-tuning, post-training, RLHF
Ph.D. in Computer Science or a closely related field
Prior LLM research experience
Comfortable programming in Python and familiar with frameworks such as PyTorch and HuggingFace
Preferred Qualifications:
Publications at peer-reviewed conferences such as NeurIPS, ICLR, ICML, etc.
Experience with large-scale LLM training, particularly in a distributed computing environment
Competitive salary
Health care plan (medical, dental, and vision)
Retirement plan (401k, IRA) with employer matching
Unlimited PTO
Flexible hybrid work arrangement
Relocation assistance for qualifying employees
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In this role, you will collaborate with a small team of talented researchers on ambitious, greenfield projects in generative AI and reinforcement learning.
Core responsibilities:
Design, execute, and analyze experiments with a high degree of independence
Contribute to core models and frameworks
Create high-quality datasets (both in-the-wild and synthetic)
Perform literature reviews and implement new techniques from papers
Contribute to publications, present at conferences and workshops, etc.
Research Areas of Interest: An incomplete list of current and near-term research directions:
Contrastive representation learning
Steerability and guided decoding
Tractable probability models
Code-specific architectures
LLM fine-tuning, post-training, RLHF
Ph.D. in Computer Science or a closely related field
Prior LLM research experience
Comfortable programming in Python and familiar with frameworks such as PyTorch and HuggingFace
Preferred Qualifications:
Publications at peer-reviewed conferences such as NeurIPS, ICLR, ICML, etc.
Experience with large-scale LLM training, particularly in a distributed computing environment
Competitive salary
Health care plan (medical, dental, and vision)
Retirement plan (401k, IRA) with employer matching
Unlimited PTO
Flexible hybrid work arrangement
Relocation assistance for qualifying employees
#J-18808-Ljbffr