Mission Statement

Through single-cell biology, computational biology, and artificial intelligence, Immunai is reprogramming the immune system to develop improved drugs and therapeutics.

Background

Immunai was founded in December 2018. We are a multidisciplinary team of immunologists, genomics technologists, computer scientists, engineers, mathematicians and operators singularly focused on solving an audacious challenge. Backed by some of the leading scientific minds in immunology, oncology, and single cell genomics, we are up for the challenge.

Immunai has begun the process of mapping millions of immune cells and their functions in order to build a proprietary data set for clinical immunological data. The immune system mapping will play an important role in boosting R&D efforts in developing targeted therapies for different diseases. The company noted that the immune system consists of trillions of cells that are divided into different cell types that perform different functions. Because of the complexity of the system, it can be difficult to determine how drugs will interact with the immune cells. This increased understanding can be used to boost clinical response to different cellular therapies.

Immunai’s mission is to map the immune system with neural networks and transfer learning techniques informed by deep immunology knowledge. We developed the tools and know-how to help every immuno-oncology and cell therapy researcher excel at their job. This helps increase the speed in which drugs are developed and brought to market by elucidating their mechanisms of action and resistance.

Location - New York, New York

Reporting to- Director of Machine Learning

Your Role

You will play an important role in Immunai’s Algorithms Research team researching, building, and deploying our core Immunai Sample Representation models to help understand and synthesize large amounts of single-cell immunological data, bridging therapies, indications, data modalities, and biological systems (in vitro, in vivo, in sapiens). You will collaborate very closely with computational biologists and immunologists to ensure the resulting product is successful and used throughout Immunai’s internal and external projects.

Responsibilities

• Research, develop, and deploy both state-of-the-art and classic machine learning models based on single-cell data from immunological datasets
• Develop and own scalable production pipelines driven by the core models.
• Stay updated on latest advancements in single-cell omics and machine learning research.
• Collaborate with stakeholders across software engineering, computational biology, and immunology to solve shared R&D objectives.
  
  

Requirements:

Required Qualifications

• Ph.D. in computer science, bioengineering, computational biology, or related discipline
• Demonstrated experience using custom deep learning models to solve biological problems
• Demonstrated experience working with large amounts of biological data, preferably with single-cell omics data
• Strong publication track record with at least one high-quality first-authored publication applying ML to biological problems
• Strong technical abilities in Python and ML libraries (sklearn, PyTorch), preferably some experience in R
• Evidence of engagement with open source projects and community
• Excellent written and verbal communication skills.
  
  

Desired personal traits:

• You want to make an impact on humankind
• You prioritize “We” over “I”
• You enjoy getting things done - with super high quality
• You collaborate effectively with people of diverse backgrounds and cultures
• You have superior skills and experience, and constantly strive to be world-class in what you do
• You constantly challenge your own assumptions, pushing for continuous improvement
• You have a growth mindset
• You make decisions that favor the company, not yourself or your team
• You are candid, authentic, and transparent
  
  

Compensation: This position offers a base salary typically between $140,000 and $170,000. There is an opportunity to consider higher compensation above this range based on business need, candidate experience, and or skills.