Business Development Intern

At NE47 Bio, we are building the next generation of protein language models and machine-learning tools to enable biologists to understand and design proteins with unprecedented ease and accuracy. Our OpenProtein.AI platform places state-of-the-art sequence-to-function prediction and protein design tools, powered by our large protein language models and Bayesian machine learning frameworks, directly into the hands of biologists and protein engineers.

In natural language processing, large language models, which use massive neural networks to model statistics of natural text, are redefining how companies think about search, chatbots, copywriting, text editing, code development, and more. Models like ChatGPT use massive transformer networks trained on large web text corpora to be able to respond to user queries via extending user-supplied text prompts with uncanny ability. These responses are based on learning natural statistical patterns in the training text and generating responses based on the probability assigned to each following word given some prefix text. The remarkable capabilities of these models have been unlocked by the ability to scale transformer language models to massive sizes using huge amounts of GPU computing and to train them on enormous text corpora scraped from the internet.

Much like natural language, proteins are sequences of amino acids that fold into three dimensional structures in order to carry out the vast majority of functions at the molecular level of life. Proteins are responsible for converting sunlight into energy, reading and replicating DNA, transporting nutrients in and out of the cell, forming the protective envelopes of viruses, identifying foreign pathogens, and transmitting and receiving signals between cells and organisms, among many others. All of these functions are determined by the unique sequence of amino acids that makes up each protein. Also much like natural language, we now have enormous databases containing the amino acid sequences of natural, functional proteins. Although the vast majority of these proteins have not been characterized (we only know their sequences), it turns out that statistical analysis of just these sequences can reveal evolutionary pressures, and, therefore, structural and functional characteristics. Over the past few years, large-scale deep learning methods, like protein language models, have transformed our ability to understand and predict the structural and functional properties of proteins by learning from these evolutionary patterns. However, current protein language models and their extensions (e.g., AlphaFold2 or ESMfold) have only scratched the surface of what large protein language models can enable for protein design and optimization.

The objective of this project is to work with our business development team to identify opportunities for growth, perform research to inform business strategy, and to develop marketing and sales materials.

Responsibilities:

As an intern with the business development team at NE47 Bio, you will work with our CEO and Business Development Lead to:

• Contribute to the business development strategy and develop plans for identifying and pursuing new business development opportunities.
• Conduct research on emerging business trends, the competitive landscape, and key companies for potential partnerships.
• Contribute to assessments and business framework development for new opportunities.
• Provide input into deal structures and financial terms.
• Build and/or support product/market and business opportunity financial models.

Some expected deliverables and responsibilities include

• Identifying and maintaining a list of potential customers and contacts.
• Evaluating and summarizing the competitive landscape.
• Building and presenting slides/business cases summarizing key relevant information to enable business development decision-making.