Machine Learning Scientist, LLM Training & Inference Research
Skills
- Strong background in LLM training and deployment.
- Research experience in scalable and distributed compute techniques.
- Familiarity with RLHF, SFT, and reinforcement learning methods for language models.
- Experience building evaluations and analyzing model failure modes.
- Publications or contributions to open-source frameworks are valued.
Responsibilities
- Lead research and development of post-training strategies including supervised fine-tuning (SFT), RLHF, and reinforcement learning with verifiers.
- Design and implement test-time compute strategies and efficient inference mechanisms for complex tool-use environments.
- Build scalable, reproducible evaluations to measure LLM performance on scientific reasoning tasks.
- Investigate the limits of frontier LLM approaches on domain-specific scientific tasks and quantify failure modes.
- Collaborate with cross-functional teams (research, engineering, domain scientists) to integrate models into production workflows.
Education
- PhD in Computer Science, Machine Learning, Statistics, Computational Biology, or related field preferred.
- Master's degree with substantial relevant industry or research experience considered.
Benefits
- Competitive compensation and equity
- Comprehensive health benefits
- Generous paid time off
- Collaborative, interdisciplinary research environment
- Opportunities for publication and open-source contribution
Average salary estimate
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