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Trainee Density Functional Theory Scientist Jobs

1910

Computational Theoretical Chemist III

Boston, MA · On-site

... density functional theory (DFT) approaches, molecular dynamics (MD) simulations, including both ... Strong desire to collaborate with AI scientists, data scientists, medicinal chemists, and ...

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How much do trainee density functional theory scientist jobs pay per hour?

As of Aug 30, 2026, the average hourly pay for trainee density functional theory scientist in the United States is $38.99, according to ZipRecruiter salary data. Most workers in this role earn between $33.89 and $42.55 per hour, depending on experience, location, and employer.

What does a trainee density functional theory scientist do?

A Trainee Density Functional Theory (DFT) Scientist is an early-career researcher who applies the principles of quantum mechanics to study the electronic structure of atoms, molecules, and materials. They use computational methods, particularly DFT, to predict physical properties, optimize structures, and analyze chemical reactions. Their work often involves running simulations, developing models, and interpreting data under the guidance of more experienced scientists. This role is typically aimed at graduates in chemistry, physics, or materials science who are building expertise in computational research.

What are the key skills and qualifications needed to thrive as a trainee density functional theory scientist?

To thrive as a Trainee Density Functional Theory (DFT) Scientist, a strong background in quantum mechanics, computational chemistry or physics, and at least a bachelor's or master's degree in a related field are essential. Familiarity with DFT software packages (such as VASP, Gaussian, or Quantum ESPRESSO), programming languages (like Python or Fortran), and high-performance computing environments is typically required. Analytical thinking, problem-solving ability, and effective communication are important soft skills for interpreting results and collaborating with research teams. These skills are crucial for conducting accurate simulations, advancing scientific understanding, and contributing to successful research outcomes.

What are some common challenges faced by trainee density functional theory scientists when transitioning from academic study to a professional research environment?

Trainee Density Functional Theory (DFT) Scientists often find that adapting to the pace and collaborative nature of professional research teams can be challenging after the more individual focus of academic work. In industry or large research institutions, projects typically involve tight deadlines, multidisciplinary teams, and the need to communicate findings clearly to both specialists and non-specialists. Additionally, trainees may need to quickly familiarize themselves with new software tools, high-performance computing platforms, and proprietary workflows that differ from academic resources. Embracing feedback, effective teamwork, and continuous learning are key to a smooth transition and long-term success in the field.
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Infographic showing various Trainee Density Functional Theory Scientist job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 89% Full Time, 6% Part Time, and 4% Contract. Highlights an 84% Physical, 5% Hybrid, and 11% Remote job distribution, with an average salary of $81,095 per year, or $39 per hour.

Computational Theoretical Chemist III

Boston, MA

1910
See more jobs at 1910external

Full-time

Re-posted 5 days ago

Job description

Computation is revolutionizing drug discovery. Advances in big chemical data, massive computing power, artificial intelligence, and molecular dynamics simulation are changing the way we develop new drugs. At 1910 , we put computation at the heart of drug discovery, blending expertise in computational chemistry, structural biology, pharmacology, data science, and software engineering to develop drugs for previously undruggable targets. 

Role description  

  • Own computational chemistry programs across therapeutic modalities, disease targets, and indications 
  • Ensure effective collaboration with the Biology and Medicinal Chemistry teams by providing key computational chemistry insights to aid in the Hit-to-Lead and Lead Optimization phases of drug discovery operations  
  • Ensure effective collaboration with the ML Engineering and AI Research team by providing key computational chemistry insights to aid in the development of AI/ML models for drug discovery as well as the incorporation of those models into drug discovery operations 
  • Teach key computational chemistry principles to your cross-disciplinary colleagues from Medicinal Chemistry, AI Research, Machine Learning Engineering, Cell Biology, and Pharmacology 
  • Manage day-to-day operations of the Computational Theoretical Chemistry Team, mentor junior staff, and represent the team in senior leadership meetings 
  • Partner to improve 1910's existing process for progressing from computational hit to experimental hit to lead to drug candidate 
  • Co-author provisional patents and peer-reviewed research papers 
  • Progress a virtual hit to a biochemical/cellular hit 
  • Validate a cellular hit in a clinically relevant animal model of disease 
  • Update provisional patents with the animal model data 
  • Nominate a lead candidate for progression into IND-enabling studies 
  • Attend and present research at conferences and events related to computational modeling in drug discovery 

Qualifications  

  • Ph.D. in computational chemistry or related discipline 
  • 3+ years of relevant industry experience within drug discovery or biotechnology 
  • Played a key role in advancing a drug discovery program from early research phases to clinical development. 
  • In-depth knowledge and hands-on experience with quantum chemical (QC) methods, including semi-empirical and density functional theory (DFT) approaches, molecular dynamics (MD) simulations, including both standard MD and enhanced sampling techniques such as metadynamics, umbrella sampling, and replica exchange MD, free energy simulations such as FEP and TI, and QM/MM methodologies for small and large molecular systems 
  • Strong understanding of key concepts, including potential energy surfaces (PES), intermolecular and intramolecular forces/interactions, force fields, molecular properties, thermodynamic properties, solvation models (implicit/explicit), and conformational sampling 
  • Proficiency in analyzing molecular properties such as solvation free energy, dipole moments, vibrational frequencies, electrostatic potential, charge distribution, and more. 
  • Deep knowledge of implicit and explicit solvent models, with extensive experience modeling solvent effects on molecular systems and chemical reactions in various environments 
  • Extensive experience in using and troubleshooting software tools for QC calculations (e.g., ORCA, xTB, CREST, etc.), MD simulations (e.g., GROMACS, OpenMM, etc.), Drug Design Development Packages (e.g., EG, Schrodinger, MOE, CRESSET) 
  • Experience working with HPC Clusters and cloud-based services like (e.g., Microsoft AZURE, AWS) 
  • Ability to optimize computational simulation protocols for efficient resource usage 
  • Proven experience working with small organic molecules and large biomolecular systems (e.g., peptides, proteins, etc.) for property prediction, conformational analysis, and structure-activity relationships (SAR) 
  • Hands-on experience with Python and Bash scripting for automating workflows and data analysis 
  • Familiarity with cheminformatics toolkits such as RDKit for molecular property prediction and data management 
  • Basic knowledge of machine learning (ML) techniques applied to molecular property prediction, virtual screening, and related tasks 
  • Strong desire to collaborate with AI scientists, data scientists, medicinal chemists, and biologists to interpret computational results and guide experimental design 
  • Clear and effective communication of complex scientific ideas through reports, presentations, and publications 

Nice to Haves 

  • Publications in computational chemistry related to drug discovery 

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