Company Overview: Terray Therapeutics is a venture-backed biotechnology company led by pioneers and long-time leaders in artificial intelligence, synthetic chemistry, automation, and nanotechnology. We’re generating chemical data purpose-built to propel drug discovery into the information age — and we’re doing it on a larger scale and faster than has ever before been possible.

Our closed loop system generates precise chemical datasets at unrivaled scale that work seamlessly with AI to systematically map biochemical interactions between small molecules and causes of disease. Iterative cycles of virtual molecular design and experimentation power AI and machine learning models, which in turn guide the next cycle of design. With a chemistry engine that measures billions of interactions daily and becomes increasingly precise with every cycle, we can answer an unprecedented array of questions — deriving insights that enable us to predictably create drugs for patients in need.

Position Summary: 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.

The core responsibilities of this position are:

• 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

Experience and Qualifications: Part of Terray’s success is nurtured by a hands-on work environment where everyone is accountable, vested in a vision of excellence, and actively taking part in the success of the business. Terray supports a positive work environment where employees can feel engaged, recognized, and empowered to be creative.

Required Qualifications:

• 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

Preferred Qualifications:

• 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).

Compensation Details: $140,000 - $216,000 (annually) depending on experience; participation in the Company's option plan; 3% retirement safe harbor contribution; fully-paid medical, dental, vision, life, and disability benefits.