Machine Learning Force Fields Scientist at Schrödinger

Portland, New York, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Life Sciences, Materials ScienceIndustries

Requirements

PhD (or extensive experience) in Chemistry, Materials Science, Engineering, Computer Science, or Physics
Proven track record of scientific contribution and independent research
Prior experience with development of ML force fields and/or electronic structure methods
Background in physical science
Deep knowledge of finite system and periodic DFT, as well as other electronic structure methods, including their limitations and appropriate applications
Proficient Python programmer with prior knowledge of ML toolkits such as Scikit-Learn, NumPy, SciPy, Pandas, and PyTorch

Responsibilities

Build and manage large data sets generated using quantum chemical methods at scale to develop predictive ML force fields
Develop software that trains and applies ML force fields to challenging problems in life and materials sciences
Extend the accuracy, capability, and generalization of current ML force fields
Communicate results and present ideas to the team

Skills

Python

PyTorch

Scikit-Learn

NumPy

SciPy

Pandas

DFT

Quantum Chemistry

Electronic Structure Methods

Machine Learning

Force Fields

Schrödinger

Computational platform for biopharmaceutical research

About Schrödinger

Schrödinger provides a computational platform that aids in the research efforts of biopharmaceutical companies, academic institutions, and government laboratories around the world. Their platform offers advanced computational tools that help in drug discovery and development across various therapeutic areas. Schrödinger's products work by using sophisticated algorithms and simulations to predict how different compounds will interact, which can significantly speed up the research process. Unlike many competitors, Schrödinger not only licenses its software but also engages in collaborative research and drug discovery programs, allowing for a more integrated approach to scientific research. The company's goal is to enhance scientific research and development through its platform, ultimately leading to the discovery of new drugs and therapies.

New York City, New YorkHeadquarters

1990Year Founded

$362.7MTotal Funding

IPOCompany Stage

Government & Public Sector, Enterprise Software, BiotechnologyIndustries

501-1,000Employees

Benefits

Health Insurance

Dental Insurance

Vision Insurance

401(k) Company Match

401(k) Retirement Plan

Flexible Work Hours

Remote Work Options

Paid Vacation

Parental Leave

Risks

Recursion and Exscientia merger creates strong AI-driven drug discovery competitor.

Avicenna's independent expansion could lead to competitive tensions with Schrödinger.

High R&D costs may strain resources despite Gates Foundation's $10M grant.

Differentiation

Schrödinger's platform supports diverse clients, including biopharma, academia, and government labs.

The company excels in rapid drug discovery, exemplified by SGR-1505's swift development.

Schrödinger's collaboration with Avicenna enhances medicinal chemistry through machine learning.

Upsides

$19.5M Gates Foundation grant boosts platform for neglected diseases, expanding market reach.

Predictive toxicology tools initiative could lead to safer, efficient drug development.

SGR-1505 discovery showcases ability to reduce drug discovery timelines significantly.

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