Scientist, Computational Chmistry

Terray Therapeutics

2d•$140,000 - $216,000

About The Position

We are looking for a highly motivated scientist, ideally with a recent Ph.D. and a few years of postdoctoral or industry experience, to help drive our computational drug discovery and methods development efforts. In this role, you will be responsible for the implementation, rigorous benchmarking, and routine application of state-of-the-art computational chemistry methods. You will work directly with leadership to help expand and maintain a robust computational pipeline that integrates physics-based simulations and semi-empirical quantum mechanics calculations to guide molecular design. Special emphasis will also be placed on coordination and integration with machine learning methods, where the tools are meant to both generate synthetic data for ML models and augment them in parallel. This position will be part of the molecular design team, working closely with informatics, design, molecular data, and machine learning teams, all part of the computational and data sciences (CDS) team at Terray Therapeutics. This is a hands-on role for a scientist who is passionate about applying first-principles thinking to solve challenging problems in drug discovery, and is eager to work in a collaborative, fast-paced environment.

Requirements

BS/MS/Ph.D. in computational chemistry, theoretical chemistry, chemical physics, biophysics, or a related field
Experience setting up, running, and analyzing molecular dynamics and free energy simulations (e.g., FEP, ABFE, OpenMM) and quantum chemistry methods (xTB)
Proficiency in Python programming for data analysis, workflow automation, tool development, cloud deployment, and databasing methods
A strong understanding of the physical principles underlying molecular interactions, thermodynamics, and kinetics in the context of protein-ligand binding
Excellent problem-solving skills, the ability to work both independently and as part of a collaborative team, and strong attention to detail in both code syntax and simulation setup details

Nice To Haves

1-3 years of experience as a computational chemist in the biopharma industry or a relevant post-doc
Familiarity with cheminformatics toolkits like RDKit, knowledge of containerization technologies (e.g., Docker), experience working in cloud compute environments (AWS Batch, S3)
Experience with Python for database interaction and management (e.g., using Python libraries like psycopg2, sqlite3, or ORMs like SQLAlchemy to interact with SQL databases)
Familiarity with machine learning for drug discovery methods such as protein co-folding models and potency prediction models (Boltz, OpenFold etc).

Responsibilities

Implement, Validate & Benchmark a suite of computational chemistry methods, including absolute and relative binding free energy calculations (ABFE/RBFE), enhanced sampling molecular dynamics methods, and semi-empirical quantum mechanical methods (SEQM). The role will involve using open-source code libraries OpenMM, OMSF, RDKit, xTB, and custom in-house code libraries written in Python
Develop & Maintain Free Energy Calculation Deployment Code and Database with cloud deployment capable of continuous method calibration across multiple projects that can support multiple users. This will involve utilizing best practices in Python-based workflows, knowledge of cloud-based deployment infrastructure (AWS Batch), and robust database strategies
Project Impact: Apply established methods to active drug discovery projects, analyzing data, interpreting results, and providing actionable insights to guide molecular design and medicinal chemistry efforts
Stay Current: Continuously survey the scientific literature to identify and champion emerging methods and technologies in computational chemistry and AI-driven drug discovery