Senior/Principal AI Research Scientist
$210,000–$350,000 year
On-site · San Francisco, California, United States
Job Summary
Senior/Principal AI Research Scientist at Edison Scientific in San Francisco (Dogpatch) focusing on developing models and algorithms to accelerate scientific research. Responsibilities include framing AI-agent challenges as statistical problems, post-training of LLMs for complex scientific reasoning, exploring optimal sampling and quality-diversity, owning end-to-end research projects from scientists to engineers, developing and extending the experimentation platform, and collaborating with a multidisciplinary team of AI researchers, engineers, biologists, and chemists.
Required Qualifications
- 6-10+ years of strong track record of ML research on real-world problems in (but not limited to) reinforcement learning, representation learning, and Bayesian methods
- Fluency in PyTorch, Jax, or equivalent framework
- Experience with large-scale training and experimentation technologies (e.g. Ray, verl, Megatron-LM, etc.)
- Experience working with data and ML systems: EDA, feature engineering, model development, model deployment, and validation in production systems.
- Familiarity with modeling in PyTorch, Jax or equivalent framework.
- Demonstrated experience with experimentation in academic or industry settings.
- Strong programming expertise with the capability to adapt to various technical challenges in the data, ML, and LLM software stack.
- Bonus points for PhD in Machine Learning, Computer Science, or other quantitative field
- Familiarity with leveraging and managing distributed systems
Desired Qualifications
- 6-10+ years of strong track record of ML research on real-world problems in (but not limited to) reinforcement learning, representation learning, and Bayesian methods
- Fluency in PyTorch, Jax, or equivalent framework
- Experience with large-scale training and experimentation technologies (e.g. Ray, verl, Megatron-LM, etc.)
- Experience working with data and ML systems: EDA, feature engineering, model development, model deployment, and validation in production systems.
- Familiarity with modeling in PyTorch, Jax or equivalent framework.
- Demonstrated experience with experimentation in academic or industry settings.
- Strong programming expertise with the capability to adapt to various technical challenges in the data, ML, and LLM software stack.
- Bonus: PhD in Machine Learning, Computer Science, or other quantitative field
- Bonus: Familiarity with leveraging and managing distributed systems
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