The Rundown AI, Inc.
Research Scientist, Life Sciences
The Rundown AI, Inc., San Francisco, California, United States, 94199
About the Role
We're seeking an exceptional Research Scientist to join our pioneering Life Sciences team at Anthropic. Our team is building a world‑class research group focused on making fundamental biological discoveries. This role combines cutting‑edge AI with hands‑on biological research, positioning Anthropic at the forefront of AI‑accelerated scientific discovery.
As a founding member of Life Sciences, you'll work in a high‑impact team that operates at the intersection of computational and experimental biology. You'll help establish Anthropic as a leader in biology research while developing product intuition through direct engagement with the challenges and opportunities of laboratory science.
Responsibilities:
Implement a scientific research program that may include both wet‑lab and computational components
Design and implement evaluation methodologies for assessing AI model capabilities relevant to biological research and applications
Develop and execute strategies to systematically improve model performance on scientific tasks
Develop approaches to address long‑horizon task completion and complex reasoning challenges essential for scientific discovery
Translate between biological domain knowledge and machine learning objectives
You May Be a Good Fit If You:
Have at least 8 years of hands‑on experience in life sciences R&D, with deep expertise in areas such as molecular biology, drug discovery, or computational biology
Have a track record of bridging biological domain knowledge with computational approaches to solve real scientific problems
Basic proficiency in Python and familiar with ML development practices
Are comfortable navigating ambiguity and developing solutions in rapidly evolving research environments
Can work independently while maintaining strong collaboration with cross‑functional teams
Are results‑oriented, with a bias towards flexibility and impact
Thrive in a fast‑paced research environment where you balance rigorous scientific standards with rapid iteration
Are passionate about using AI to accelerate scientific discovery while maintaining high ethical standards
Strong Candidates May Also Have:
Ph.D. in a biological science (molecular biology, biochemistry, bioengineering, computational biology), or in a related field, or equivalent industry experience
Published research or practical experience in scientific AI applicationsFamiliarity with modern machine learning techniques and model training methodologies
Familiarity with biological databases (UniProt, GenBank, PDB) and computational biology tools
This role offers a unique opportunity to shape how AI transforms biological research. You'll work with some of the world's best AI researchers while tackling problems that matter deeply for human health and scientific understanding. If you're excited about using your expertise to make fundamental biological discoveries and guide the development of transformative AI systems, we want to hear from you.
Role‑specific policy:
For this role, we expect staff to be able to work from our San Francisco office at least 3 days a week, though we encourage you to apply even if you might need some flexibility for an interim period of time.
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As a founding member of Life Sciences, you'll work in a high‑impact team that operates at the intersection of computational and experimental biology. You'll help establish Anthropic as a leader in biology research while developing product intuition through direct engagement with the challenges and opportunities of laboratory science.
Responsibilities:
Implement a scientific research program that may include both wet‑lab and computational components
Design and implement evaluation methodologies for assessing AI model capabilities relevant to biological research and applications
Develop and execute strategies to systematically improve model performance on scientific tasks
Develop approaches to address long‑horizon task completion and complex reasoning challenges essential for scientific discovery
Translate between biological domain knowledge and machine learning objectives
You May Be a Good Fit If You:
Have at least 8 years of hands‑on experience in life sciences R&D, with deep expertise in areas such as molecular biology, drug discovery, or computational biology
Have a track record of bridging biological domain knowledge with computational approaches to solve real scientific problems
Basic proficiency in Python and familiar with ML development practices
Are comfortable navigating ambiguity and developing solutions in rapidly evolving research environments
Can work independently while maintaining strong collaboration with cross‑functional teams
Are results‑oriented, with a bias towards flexibility and impact
Thrive in a fast‑paced research environment where you balance rigorous scientific standards with rapid iteration
Are passionate about using AI to accelerate scientific discovery while maintaining high ethical standards
Strong Candidates May Also Have:
Ph.D. in a biological science (molecular biology, biochemistry, bioengineering, computational biology), or in a related field, or equivalent industry experience
Published research or practical experience in scientific AI applicationsFamiliarity with modern machine learning techniques and model training methodologies
Familiarity with biological databases (UniProt, GenBank, PDB) and computational biology tools
This role offers a unique opportunity to shape how AI transforms biological research. You'll work with some of the world's best AI researchers while tackling problems that matter deeply for human health and scientific understanding. If you're excited about using your expertise to make fundamental biological discoveries and guide the development of transformative AI systems, we want to hear from you.
Role‑specific policy:
For this role, we expect staff to be able to work from our San Francisco office at least 3 days a week, though we encourage you to apply even if you might need some flexibility for an interim period of time.
#J-18808-Ljbffr