You will

• Work on MD scoring improvements such as force field development, solvation energy calculations, and QM/MM techniques, that will integrate with our sampling / simulation pipelines and ML models for absolute binding free energy prediction
• Improve the accuracy of our ML/MD predictors on our multi-target potency benchmarks, consisting of thousands of carefully curated potency datapoints and protein-ligand structures across many challenging, high-value protein targets from a diverse set of target classes
• Utilize our cluster of 1,000s of GPUs and 10,000s of CPUs for massive experimentation, with an easy-to-use Python API that manages autoscaling, parallelization, and data pipelining behind the scenes
• Work closely with our ML researchers, computational chemists, and medicinal chemists to validate and quickly deploy improvements to our ML/MD platform for our chemists' daily use on our internal drug programs and collaborations

You are

• A PhD-level researcher with publications in the field of free energy calculation or force-field development, preferably for proteins and/or small molecules
• Proficient in statistical mechanics, with a solid understanding of the theoretical and practical trade-offs between various scoring and sampling methods, for the current state-of-the-art absolute and relative binding free energy calculation methods
• A skilled programmer, preferably in Python, such that you can quickly implement your research ideas and analyze their performance across our benchmark suite

What we offer

• The opportunity to work on challenging, high impact computational chemistry research that is immediately deployed to accelerate the discovery of new medicines
• A world-class, tight-knit team of good-hearted people across software, machine learning, computational chemistry, medicinal chemistry, and biology
• Competitive salary and equity. Medical, dental, and vision insurance, and a 401(k) program