Senior Computational Chemist

Job Description OverviewWe are seeking a high-agency Senior Computational Chemist to join PHIN. Your responsibility will be to constantly improve the architectural backbone of our materials discovery pipeline, bridging the gap between high-fidelity quantum chemistry and scalable machine learning. This role isn't just about running calculations; it's about building the "plumbing" that allows us to explore the vast chemical space of materials at unprecedented speeds.Core ResponsibilitiesPipeline Automation: Design and implement automated workflows for DFT and Quantum Chemistry (QC) calculations to generate high-quality training data.ML Potential Development: Develop and deploy Machine Learning Potentials to enable large-scale simulations of industrially relevant systems and porous materials.Generative Modeling: Utilize generative models to bridge data generator and machine learning training length scales.Active Learning: Improve our active learning loops to minimize manual intervention and maximize the informational value of every CPU/GPU hour spent.Candidate ProfileExperience: 5–8 years of experience in computational chemistry, materials science, or a related field, with a proven track record of studying catalysts, polymers and MOFs.Generalist Capability: A "Swiss Army Knife" mentality—equally comfortable debugging a Python script, optimizing a basis set, or configuring a cloud cluster.HPC Familiarity: Extensive experience with SLURM and containerization (Docker/Singularity) for distributed computingTool Agnostic: Skilled in multiple software packages (e.g., QE, VASP, CP2K, ORCA, Psi4) and able to pick up new tools rapidly based on the problem at hand.AI-Native: Deep intuition for how machine learning can accelerate traditional physics-based simulations, specifically regarding generative chemistry.ML Collaboration: Ability to work closely with data scientists to translate chemical problems into machine-learnable frameworks.Application QuestionnairePlease provide concise answers to the following questions as part of your application:Automation: Describe a time you had to build a custom data pipeline or automation tool because existing software (e.g., QE, VASP, Gaussian) didn't support your specific workflow. What was the biggest bottleneck?ML/Physics Trade-offs: When developing a Machine Learning Interatomic Potential (MLIP) for a complex system like a MOF, how do you decide when to prioritize more DFT training data versus increasing model complexity?Generative Modeling: What is your experience in developing and using generative materials models?Tool Agnosticism: Suppose we need to switch our primary simulation engine to a tool you've never used before (e.g., switching from CP2K to ORCA). Walk us through your process for becoming proficient and validating your results in this new environment within two weeks.Scaling & Infrastructure: Describe your experience managing high-throughput calculations. How do you handle "job failures" at scale (e.g., 10,000+ simultaneous DFT runs) to ensure the integrity of the final dataset?Values Alignment: Rate yourself 1-5 on the embodiment of the following values and give a 1 sentence rationale for each: People-first, Constant Improvement, Efficiency, High Quality, Visionary