Scientist/ Senior Scientist, Computational Chemistry – Molecular Dynamics and Drug Design

The Position

We are seeking a highly skilled Computational Chemist with strong molecular dynamics expertise to join our dynamic team in Madison, Wisconsin. In this on-site role, you will apply MD simulations to accelerate oligonucleotide drug development, advance predictive power of AI/ML models and explore novel spaces in oligonucleotide drug design. You will work closely with a cross-functional R&D team at the cutting edge of RNA interference (RNAi), where your computational insights will directly impact the development of life-changing therapies.

Responsibilities

• Molecular Dynamics Simulation of RNA protein interactions: Analyze large biological datasets (sequences, structures, activity data) to uncover patterns and insights that inform lead discovery. Apply state-of-the-art methods to elucidate mechanisms of RNA protein interaction that underpin empirical understanding of gene silencing.
• Machine Learning R&D: Apply learnings to aid in creating AI/ML frameworks and explore novel chemical space in oligonucleotide drug design. Cross-Functional
• Collaboration: Work closely with chemists, biologists, and other scientists to integrate computational designs with experimental validation. Propose candidates and provide in-silico rationale for compounds to be synthesized or biologically tested, and iteratively improve designs based on lab results.
• Innovation & Continuous Learning: Stay up-to-date with the latest research and advancements in AI/ML for drug discovery (e.g. new algorithms, frameworks, and scientific publications). Evaluate and integrate new tools or methodologies (for example, improved protein structure prediction algorithms or generative models) to continually enhance the team’s capabilities.

Requirements

• Ph.D. in a relevant field (Computational Chemistry, Physics, Biochemistry, Bioinformatics, Computer Science, or related discipline) with a focus on computational approaches in chemistry or biology. Research focus in molecular dynamics is preferred. Candidates with 0–10 years of postdoctoral or industry experience (including recent PhD graduates) are encouraged to apply.
• Molecular Dynamics Expertise: Demonstrated experience in physical modeling and molecular dynamics (MD) simulations using GROMACS, AMBER, OpenMM, or similar tools. Solid understanding of algorithms for modeling molecular structures or properties, and familiarity with statistical modeling and data science in a scientific context.
• Programming Skills: Proficiency in Python programming and common scientific computing libraries. Ability to develop and debug code for dynamic workflows; experience with version control (Git) and reproducible research practices. Experience in scripting and workflow automation for simulation setup, monitoring, and analysis.
• Collaborative Skills: Excellent problem-solving abilities and communication skills. Proven ability to work both independently and as part of an interdisciplinary team, effectively communicating computational findings to collaborators from chemistry and biology backgrounds.

Preferred

• Experience in combining physics-based modeling approaches with machine learning or AI is a strong plus.
• Demonstrated expertise in applying free energy calculation methods—such as FEP, TI, MM/PBSA, or MM/GBSA—to biologically and pharmaceutically relevant targets, with an emphasis on quantitative binding affinity prediction or selectivity profiling.
• Proven experience leveraging molecular dynamics simulations to support drug discovery efforts in an industry setting, including applications in lead optimization, target validation, and mechanistic hypothesis testing.