Senior Computational Biologist (Remote) at Output Biosciences

Remote

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$125,000 – $210,000Compensation

Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Biotechnology, Artificial Intelligence, HealthcareIndustries

Requirements

Candidates must possess a PhD in a biological field and demonstrate strong programming skills in Python. Experience with computational biology tools such as structure prediction, molecular docking, and virtual screening is required, along with experience working with Protein or Chemical Language Models. Bonus points include hands-on experience building AI/ML using PyTorch and Lightning, experience with cloud computing platforms like AWS, hands-on biology laboratory experience, contributions to peer-reviewed publications in computational biology, and knowledge of machine learning techniques applied to biological data. The ideal candidate is collaborative, passionate about using data science to unlock biology's secrets, proactive in problem-solving, possesses a mature mindset in ambiguous situations, and has excellent communication skills. Periodic travel to NYC HQ is expected.

Responsibilities

The Senior Computational Biologist will develop AI systems for complex biological reasoning across multiple scales, working alongside research team members and founders. Responsibilities include developing machine learning methods to extend the predictive and generative platform, designing and executing computational pipelines to validate and prioritize de novo generated molecules using state-of-the-art tools, and using deep biological understanding to design novel therapeutics for hard-to-target proteins with generative models. The role also involves analyzing and integrating large-scale biological datasets, contributing directly to the development of a biologically-aware AI platform, and transforming the landscape of medicine.

Skills

Python

Computational Biology

Structure Prediction

Molecular Docking

Virtual Screening

Protein Language Models

Chemical Language Models

Machine Learning

Data Science

Biological Reasoning

Generative Models

Output Biosciences

Develops preventative therapies for chronic diseases

About Output Biosciences

Output Biosciences focuses on developing preventative therapies aimed at extending human healthspan by addressing chronic diseases such as diabetes and heart disease. The company combines biotechnology with artificial intelligence to create therapies that can be discovered and manufactured more quickly and safely than traditional methods. This allows them to bring new treatments to market in just a few months, which is much faster than the usual drug development process. Their main clients include healthcare providers, pharmaceutical companies, and research institutions seeking effective solutions for chronic disease management. Output Biosciences differentiates itself by leveraging computational biology to streamline therapy development and commercialization, generating revenue through partnerships and licensing. The goal of Output Biosciences is to fundamentally change healthcare by preventing chronic diseases before they occur.

New York City, New YorkHeadquarters

2020Year Founded

$125KTotal Funding

PRE_SEEDCompany Stage

AI & Machine Learning, Biotechnology, HealthcareIndustries

1-10Employees

Risks

Increased competition from AI-driven biotech startups threatens market share.

Regulatory scrutiny on AI applications may delay approval processes.

Rapid technological advancements may render current methods obsolete without continuous innovation.

Differentiation

Output Biosciences integrates AI and computational biology for rapid therapy development.

The company focuses on preventative therapeutics to extend human healthspan.

Output Biosciences accelerates drug development timelines, bringing therapies to market in months.

Upsides

Growing interest in AI-driven drug discovery boosts investment and partnerships.

Rise of personalized medicine creates opportunities for tailored AI-integrated therapies.

FDA's acceptance of AI-driven drug development enables faster regulatory pathways.

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