Output Biosciences

Machine Learning Research Scientist, Generative Biology

New York, New York, United States

Not SpecifiedCompensation
Junior (1 to 2 years)Experience Level
Full TimeJob Type
UnknownVisa
Artificial Intelligence, BiotechnologyIndustries

Requirements

Candidates should possess a Bachelor's degree in Computer Science, Computer Engineering, Mathematics, Physics, or a related technical field, or a PhD with 2 years of research experience, or a Bachelor's degree with 4+ years of research or industry experience in Deep Learning, Artificial Intelligence, or other relevant fields, and a strong publication record in top-tier AI conferences or journals such as NeurIPS, ICML, or ICLR. They must have extensive hands-on experience building and training generative models, including diffusion models, GANs, VAEs, normalizing flows, or autoregressive models, and proficiency in Python and expertise in at least one major deep learning framework like PyTorch, TensorFlow, or JAX.

Responsibilities

As a Generative AI Research Scientist, you will design and develop cutting-edge generative models specifically for biological data, creating AI systems that can generate novel molecular structures, protein designs, cellular components, and biological pathways. You will train and scale innovative AI models using large, diverse biological multi-modal datasets across distributed GPU clusters, solve challenging AI research problems at scale, tackling key aspects such as model architecture and scaling, pre-training and fine-tuning strategies, and rigorous evaluation methods. Your work will span various advanced generative AI architectures, including transformers, diffusion models, and autoencoders, exploring and implementing these techniques to push the boundaries of biological AI, and ultimately build the world's first biological reasoning model.

Skills

Python
PyTorch
TensorFlow
JAX
Diffusion Models
GANs
VAEs
Normalizing Flows
Autoregressive Models
Transformers
Deep Learning
Generative Models
Biological Data
Molecular Structures
Protein Designs
Cellular Components
Biological Pathways

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.

Key Metrics

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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