Computational Chemistry Expert (PhD)

Job Summary

The Computational Chemistry Expert (PhD) acts as a high-level data validator and workflow auditor within the micro1 core scientific training pipeline. Core daily responsibilities include evaluating and critiquing advanced quantum chemistry and molecular simulation workflows generated by AI agents; assessing and optimizing simulation topologies and engine configurations (such as Gaussian, ORCA, or GROMACS configurations) for absolute physical validity; analyzing data from molecular dynamics and drug discovery pipelines to uncover subtle runtime or logical flaws; writing highly disciplined step-by-step rationales of chemical phenomena; and maintaining analytical evaluation scripts using Python and cheminformatics libraries like RDKit.

Foundational Capabilities & Prerequisites

• Academic Preparation: A completed PhD in Chemistry, Computational Chemistry, Molecular Modeling, biophysics, or a closely related quantitative field (or equivalent high-level corporate R&D research history).
• Simulation Engine Mastery: Deep practical expertise within simulation-heavy environments and direct exposure to industry-standard quantum chemistry or molecular dynamics software platforms.
• Core Tool Portfolio: Hands-on familiarity with tools across at least two of the following software families:
  • Quantum Chemistry / Electronic Structure: Gaussian, ORCA, Psi4, NWChem.
  • Molecular Dynamics (MD): GROMACS, LAMMPS, AMBER.
  • Cheminformatics & Utilities: RDKit, OpenBabel.
• Scripting & Automation Fluency: Strong scientific programming habits, particularly in building automated data transformation and parsing pipelines using Python.
• Quantitative Analytical Reasoning: Outstanding scientific reasoning and mathematical problem-solving skills, with a track record of diagnosing subtle errors in electronic structure calculations, basis set choices, or force field parameters.
• Documentation Precision: Superb written and verbal English communication skills, allowing you to explain highly complex physical chemistry, thermodynamics, and quantum concepts with exact clarity for AI training text pools.
• Remote Self-Direction: Exceptional time management skills, with the ability to deliver high-quality scientific insights independently within a remote contract setup.

Preferred Contextual Multipliers

• AI/ML-Assisted Chemistry: Past familiarity with machine-learning force fields (MLFFs), active learning frameworks, or AI-driven chemical retrosynthesis benchmarking.
• Academic Footprint: A track record of peer-reviewed publications, industrial patents, or active contributions to open-source scientific software packages.
• Applied Discovery Domain: Specializations in target-based drug discovery, spectroscopy analysis, or automated reaction prediction mechanics.

Key Responsibilities

1. High-Level Scientific Workflow Auditing

• Evaluate, grade, and optimize complex computational chemistry workflows, input decks, and analysis scripts produced by automated AI systems.
• Spot subtle errors in quantum mechanical calculations, such as incorrect spin multiplicities, bad basis set pairings, or unphysical geometries.
• Review structural topologies, boundary settings, and integration time-steps within molecular dynamics runs to guarantee absolute physical accuracy.

2. Dataset Enrichment & Advanced Problem Engineering

• Create and document specialized chemical engineering problems, reaction mechanisms, and multi-step simulation scenarios to expand AI evaluation datasets.
• Design technical evaluation guidelines that test an AI agent's ability to automate complex chemical calculations and retrosynthesis pathways.
• Build reproducible Python-based test scripts to confirm chemical data stability across different cheminformatics software packages.

3. Clear Technical Documentation & Scientific Rationale Design

• Write detailed, step-by-step scientific rationales explaining corrections to broken or unoptimized simulation runs.
• Author clear, detailed guides that explain the exact mathematical and physical reasons why a particular simulation configuration works well or fails.
• Translate highly complex physical chemistry principles and electronic structure theories into well-structured Markdown datasets to support reinforcement learning.

4. Cross-Functional Quality Alignment

• Work within a distributed global network of doctoral-level scientists to hit dataset deadlines and ensure high data quality across scientific categories.
• Engage in fast-moving feedback loops, updating your review style to match shifting AI training goals and model performance criteria.
• Use git repositories and version control tools to manage evaluation workflows, update tracking systems, and document codebase changes.

Skills Required:

• Chemistry / Chemical / Engineering