Calliere Group
Senior Machine Learning Scientist, Biological Modeling
Calliere Group, Raleigh, North Carolina, United States, 27601
Senior Machine Learning Scientist, Biological Modeling
A well‑funded, mission‑driven research and development organization in Boston is looking for a talented Machine Learning Scientist or Engineer to join our team. We focus on tackling the most challenging questions in human biology, specifically the mechanisms of aging, to create therapeutic interventions that extend human healthspan.
We foster a highly collaborative environment where computational experts work alongside world‑class biologists, supported by state‑of‑the‑art technology and a commitment to foundational scientific discovery.
Opportunity You will join a multidisciplinary team dedicated to protein engineering and design. In this role, you will create and implement sophisticated machine learning algorithms to understand, model, and generate novel proteins. Your work will directly influence our experimental pipeline, collaborating closely with bench scientists to design experiments that validate and iteratively improve your computational models.
We seek a self‑directed, innovative candidate eager to push the boundaries of machine learning in biology. You will stay current with the latest academic literature, adapt novel methods to our challenges, and contribute to a culture of scientific excellence.
Core Responsibilities
Develop and apply advanced machine learning models for protein modeling and design, including generative models, language/sequence modeling, and representation learning.
Build and maintain robust data infrastructure and software tools to support the ML research lifecycle.
Partner with experimental biologists to conceptualize, execute, and analyze experiments that test computational hypotheses.
Communicate findings effectively through internal presentations, and potentially through external publications and conferences.
Contribute to complex, cross‑functional projects from ideation to execution.
Requirements Required Qualifications
An advanced degree (Ph.D., or M.S. with a minimum of two years of professional experience) in Computer Science, Computational Biology, or a similar technical field.
Proven experience applying deep learning techniques to biological data, particularly sequences or structures.
Expert‑level programming skills in Python and deep proficiency with at least one major deep learning framework (e.g., PyTorch, TensorFlow, JAX).
Exceptional analytical skills and a strong quantitative background.
Demonstrated ability to collaborate effectively in a team‑oriented, cross‑functional scientific setting.
This position is based in South San Francisco and requires onsite work at least four days per week.
Desired Additional Skills
A strong background in biochemistry, structural biology, or a related area of computational biology.
Experience using modern protein structure prediction tools, such as AlphaFold.
A history of impactful contributions to the machine learning or computational biology communities, such as peer‑reviewed publications or high rankings in ML competitions.
Previous experience working directly with wet-lab biologists or in an ML Engineer/Scientist capacity.
Seniority level: Mid‑Senior level
Employment type: Full‑time
Job function: Science
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We foster a highly collaborative environment where computational experts work alongside world‑class biologists, supported by state‑of‑the‑art technology and a commitment to foundational scientific discovery.
Opportunity You will join a multidisciplinary team dedicated to protein engineering and design. In this role, you will create and implement sophisticated machine learning algorithms to understand, model, and generate novel proteins. Your work will directly influence our experimental pipeline, collaborating closely with bench scientists to design experiments that validate and iteratively improve your computational models.
We seek a self‑directed, innovative candidate eager to push the boundaries of machine learning in biology. You will stay current with the latest academic literature, adapt novel methods to our challenges, and contribute to a culture of scientific excellence.
Core Responsibilities
Develop and apply advanced machine learning models for protein modeling and design, including generative models, language/sequence modeling, and representation learning.
Build and maintain robust data infrastructure and software tools to support the ML research lifecycle.
Partner with experimental biologists to conceptualize, execute, and analyze experiments that test computational hypotheses.
Communicate findings effectively through internal presentations, and potentially through external publications and conferences.
Contribute to complex, cross‑functional projects from ideation to execution.
Requirements Required Qualifications
An advanced degree (Ph.D., or M.S. with a minimum of two years of professional experience) in Computer Science, Computational Biology, or a similar technical field.
Proven experience applying deep learning techniques to biological data, particularly sequences or structures.
Expert‑level programming skills in Python and deep proficiency with at least one major deep learning framework (e.g., PyTorch, TensorFlow, JAX).
Exceptional analytical skills and a strong quantitative background.
Demonstrated ability to collaborate effectively in a team‑oriented, cross‑functional scientific setting.
This position is based in South San Francisco and requires onsite work at least four days per week.
Desired Additional Skills
A strong background in biochemistry, structural biology, or a related area of computational biology.
Experience using modern protein structure prediction tools, such as AlphaFold.
A history of impactful contributions to the machine learning or computational biology communities, such as peer‑reviewed publications or high rankings in ML competitions.
Previous experience working directly with wet-lab biologists or in an ML Engineer/Scientist capacity.
Seniority level: Mid‑Senior level
Employment type: Full‑time
Job function: Science
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