Second Renaissance
Arena BioWorks, located in Cambridge, MA, is a new biomedical research institute focused on uncovering the mechanisms of human disease to identify opportunities for therapeutic intervention. The Institute's private funding model allows Arena to advance novel therapeutics by forming and supporting biotech companies up to the early clinical/series B stage.
As a
machine learning engineer , you will work with a team developing generative ML models for synthetic protein design. You will develop infrastructure and code for the implementation of large language models applied to biomolecule design. The research team is investigating the tuning of DNA and protein language models for the development of therapeutically relevant DNA sequences and enzymes. The machine learning engineer will, in addition to model implementation, be involved in developing training data curation pipelines and working closely with lab scientists generating high-throughput experimental data for model evaluation and fine-tuning. The candidate is expected to thrive in a multidisciplinary and highly collaborative environment. The desired candidate is passionate about developing and implementing machine learning models for the development of therapeutic molecules. They also value collaboration with scientists across teams and scientific areas and enjoy continuously learning from their peers and colleagues. Specific Responsibilities:
Work with the computational infrastructure team to optimize model training and inference environments Adapt and implement protein and DNA language models for synthetic protein design Consult with experimental scientists to evaluate data quality and optimize lab procedures Curate, preprocess, and manage training datasets from public and internal sources Document code and communicate findings to cross-functional scientific teams and to the broader scientific community through written reports, publications, and/or internal and external research presentations Qualifications:
Degree in computer science, bioinformatics, mathematics, or related 2+ years of experience implementing machine learning models in an academic or industry setting Proficiency in deep learning model development in Python and relevant ML libraries (e.g., TensorFlow, PyTorch) Ability to work independently and as a team member Strong interpersonal and communication skills Excellent analytical and critical thinking skills, both scientifically and in the ability to prioritize tasks, set deadlines, and adapt to shifting priorities Preferred Skills:
Experience working with molecular biology data (e.g., DNA sequence, functional genomics)
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machine learning engineer , you will work with a team developing generative ML models for synthetic protein design. You will develop infrastructure and code for the implementation of large language models applied to biomolecule design. The research team is investigating the tuning of DNA and protein language models for the development of therapeutically relevant DNA sequences and enzymes. The machine learning engineer will, in addition to model implementation, be involved in developing training data curation pipelines and working closely with lab scientists generating high-throughput experimental data for model evaluation and fine-tuning. The candidate is expected to thrive in a multidisciplinary and highly collaborative environment. The desired candidate is passionate about developing and implementing machine learning models for the development of therapeutic molecules. They also value collaboration with scientists across teams and scientific areas and enjoy continuously learning from their peers and colleagues. Specific Responsibilities:
Work with the computational infrastructure team to optimize model training and inference environments Adapt and implement protein and DNA language models for synthetic protein design Consult with experimental scientists to evaluate data quality and optimize lab procedures Curate, preprocess, and manage training datasets from public and internal sources Document code and communicate findings to cross-functional scientific teams and to the broader scientific community through written reports, publications, and/or internal and external research presentations Qualifications:
Degree in computer science, bioinformatics, mathematics, or related 2+ years of experience implementing machine learning models in an academic or industry setting Proficiency in deep learning model development in Python and relevant ML libraries (e.g., TensorFlow, PyTorch) Ability to work independently and as a team member Strong interpersonal and communication skills Excellent analytical and critical thinking skills, both scientifically and in the ability to prioritize tasks, set deadlines, and adapt to shifting priorities Preferred Skills:
Experience working with molecular biology data (e.g., DNA sequence, functional genomics)
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