Machine Learning Scientist, LLM Training & Inference Research
36 Minutes ago • All levels
Research Development
Job Description
As a Machine Learning Scientist in LLM Training & Inference Research at Lila Sciences, you will lead research on how to train and serve large language models for scientific applications. This involves developing and optimizing LLM post-training strategies, designing efficient inference mechanisms for complex tool use, building scalable evaluations for scientific reasoning, and exploring frontier LLM approaches for scientific tasks and quantifying their failure modes.
Good To Have:
- Publications or contributions to open-source frameworks welcome.
- Experience applying LLMs to scientific or technical data.
- Work in collaborative cross-functional ML environments.
Must Have:
- Develop and optimize LLM post-training strategies including SFT, RLHF, and RL with verifiers.
- Design test-time compute and efficient inference mechanisms for complex tool use environments.
- Build scalable evaluations for LLM performance on scientific reasoning.
- Explore the limits of frontier LLM based approaches for scientific tasks and quantifying their failure modes.
- Strong background in LLM training and deployment.
- Research experience in scalable compute techniques.
Your Impact at Lila
As a Machine Learning Scientist in LLM Training & Inference Research, you will lead research on how we train and serve large language models for scientific applications.
What You’ll Be Building
- Develop and optimize LLM post-training strategies including SFT, RLHF, and RL with verifiers.
- Design test-time compute and efficient inference mechanisms for complex tool use environments.
- Build scalable evaluations for LLM performance on scientific reasoning.
- Explore the limits of frontier LLM based approaches for scientific tasks and quantifying their failure modes.
What You’ll Need to Succeed
- Strong background in LLM training and deployment.
- Research experience in scalable compute techniques.
- Publications or contributions to open-source frameworks welcome.
Bonus Points For
- Experience applying LLMs to scientific or technical data.
- Work in collaborative cross-functional ML environments.
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