AI Scientist

About Absci

Absci is a clinical-stage biotechnology company advancing novel therapeutics using generative AI. Our Integrated Drug Creation platform combines cutting-edge AI models with a synthetic biology data engine, enabling the rapid design of innovative therapeutics that address challenging therapeutic targets.

Absci is a global company headquartered in Vancouver, WA, and maintains offices in New York City, Switzerland, and Serbia. Learn more at  or follow us on Linked In (@absci), X (@Abscibio), and You Tube.

Absci is seeking AI Scientists to join its team of experts advancing generative and predictive modeling at the intersection of machine learning and drug discovery. As an AI Scientist, you will develop AI models that generate and evaluate antibody therapeutic candidates. We are looking for exceptional contributors with backgrounds in deep learning, protein design and engineering, natural language processing, computer vision, and molecular dynamics to develop innovative approaches to creating and assessing therapeutic antibodies in silico.

Absci’s computational teams (including its AI Scientists) develop and validate software platforms for antibody design and in silico assessment, while its Wet Lab teams experimentally validate its AI platforms, and its Drug Creation teams translate experimentally-validated AI designs into clinically-viable therapeutics.

will
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• Care as much about solving the technical problems related to designing antibody therapeutics as they do about translating solutions to the clinic;
• Use their expertise to influence the AI team's research agenda while maintaining high levels of responsibility and accountability for their work;
• Demonstrate their curiosity about the problems we are attempting to solve by developing proficiency in disciplines that fall outside their own areas of expertise, constantly increasing the scale and impact of their contributions;
• Invest in ensuring their work is accessible and interpretable to scientists with other domain expertise.

Absci offers AI Scientists a unique opportunity to not only develop novel, cutting edge machine learning models, but also apply these models in real-time to generate candidate antibody therapeutics. In particular:

• Absci provides its AI Scientists with access to industry-leading compute resources, enabling large-scale experimentation for model training and deployment.
• Absci maintains its own Wet Lab, enabling AI Scientists to validate novel modeling methods via both in silico and in vitro experimentation.
• Absci maintains and is growing its own internal pipeline of assets, enabling AI Scientists to see their work directly translated into therapeutic impact for patients.

This position can be remote, hybrid, or onsite in New York, NY or Vancouver, WA.

• Develop state-of-the-art deep learning models for structure-based antibody design, antibody sequence design, antibody-antigen co-folding, antibody-antigen binding prediction, and physics-based antibody design and evaluation
• Partner with teammates in AI Research, Platform Engineering, and Computational Biology to identify key challenges and design innovative, AI-driven solutions
• Analyze in silico and in vitro validation results to iteratively improve design and evaluation methodologies
• Deliver and publish high-impact research that advances Absci’s position as a thought leader in the field of AI antibody design

• PhD or equivalent experience in Machine Learning, Computer Science, Computational Biology, Computational Chemistry, Biophysics, or a related field
• 3+ years of research experience at the intersection of machine learning and protein design, molecular modeling, or a related field, ideally including industry experience
• Fluency in Python and Py Torch
• Comfortable with design, implementation, and evaluation of state-of-the-art AI algorithms for protein design and protein structure prediction
• Expertise in large-scale model training
• Demonstrated ability to work collaboratively in an ambitious, fast-paced, interdisciplinary environment
• Demonstrated experience presenting complex technical work to…