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Dft Scientist Jobs (NOW HIRING)

Nvidia Corporation

Senior DFT Engineer

Santa Clara, CA · On-site

$122K - $168K/yr

In addition, you will help develop and deploy DFT methodologies for our next generation products ... It's because of our work that scientists, researchers and engineers can advance their ideas. At its ...

Nvidia

Senior DFT Engineer

Santa Clara, CA · Hybrid

$122K - $168K/yr

In addition, you will help develop and deploy DFT methodologies for our next generation products ... It's because of our work that scientists, researchers and engineers can advance their ideas. At its ...

Nvidia

Senior DFT Engineer

Santa Clara, CA · Hybrid

$122K - $168K/yr

In addition, you will help develop and deploy DFT methodologies for our next generation products ... It's because of our work that scientists, researchers and engineers can advance their ideas. At its ...

Nvidia Corporation

Senior DFT Engineer

Santa Clara, CA · On-site

$122K - $168K/yr

In addition, you will help develop and deploy DFT methodologies for our next generation products ... It's because of our work that scientists, researchers and engineers can advance their ideas. At its ...

Nvidia

Senior DFT Engineer

Santa Clara, CA · Hybrid

$122K - $168K/yr

In addition, you will help develop and deploy DFT methodologies for our next generation products ... It's because of our work that scientists, researchers and engineers can advance their ideas. At its ...

Socket.dev

Sr. Design for Test (DFT) Engineer

Boston, MA · On-site

$154K - $218K/yr

... scientists, engineers, and accomplished industry leaders. Lightmatter is (re)inventing the future ... Validate DFT implementation and requirements * Proactively solve problems while managing ...

SES

Computational Materials Scientist

Woburn, MA · On-site +1

$180K - $200K/yr

This powerful combination of "AI for science" and material engineering enables batteries that can ... Conduct and oversee DFT (Density Functional Theory), MD (Molecular Dynamics), and QM (Quantum ...

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Dft Scientist information

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$42.5K

$94.4K

$153K

How much do dft scientist jobs pay per year?

As of Sep 15, 2026, the average yearly pay for dft scientist in the United States is $94,420.00, according to ZipRecruiter salary data. Most workers in this role earn between $59,000.00 and $119,000.00 per year, depending on experience, location, and employer.

What is a DFT scientist?

DFT Scientists are researchers or professionals who specialize in Density Functional Theory (DFT), a computational quantum mechanical modeling method used to investigate the electronic structure of molecules and condensed matter systems. They use DFT to predict and analyze the properties of materials, such as their electronic behavior, reactivity, and stability. DFT Scientists often work in academia, research institutes, or industry sectors like materials science, chemistry, and physics, contributing to the development of new materials or understanding fundamental physical processes. Their work can involve running simulations, developing computational models, and interpreting results to guide experiments or product development.

How does a DFT scientist typically collaborate with experimental researchers in a multidisciplinary team?

A DFT Scientist often works closely with experimental chemists, physicists, and materials scientists to interpret and predict material properties. They provide theoretical insights and computational data that guide experimental design, helping to validate hypotheses or explain unexpected results. Regular meetings and collaborative problem-solving sessions are common, ensuring that computational findings align with laboratory results and drive the project forward. This synergy accelerates innovation and often leads to joint publications or patents.

What are the key skills and qualifications needed to thrive as a DFT scientist, and why are they important?

To thrive as a DFT Scientist, you need a strong background in quantum chemistry, solid-state physics, and materials science, typically supported by an advanced degree (PhD or MSc). Proficiency in density functional theory (DFT) software packages like VASP, Quantum ESPRESSO, or Gaussian, as well as programming skills in Python or Fortran, is essential. Analytical thinking, problem-solving abilities, and effective scientific communication are crucial soft skills in this role. These competencies enable accurate computational modeling, meaningful data interpretation, and successful collaboration in research environments.

What is the difference between Dft Scientist vs Materials Scientist?

AspectDft ScientistMaterials Scientist
Required CredentialsBachelor's or Master's in Materials Science, Chemistry, or related field; experience with DFT softwareBachelor's or Master's in Materials Science, Chemistry, Physics; often includes DFT knowledge
Work EnvironmentResearch labs, computational centers, industry R&DResearch labs, manufacturing, academia, industry R&D
Industry UsagePrimarily in computational modeling, materials designBroader, including experimental and theoretical work in materials development

The main difference is that a Dft Scientist specializes in computational modeling using Density Functional Theory to study materials, while a Materials Scientist has a broader focus on both experimental and theoretical aspects of materials development. Dft Scientists often work within research teams to simulate material properties, whereas Materials Scientists may conduct experiments and analyze materials in various settings.

What are popular job titles related to Dft Scientist jobs?

For Dft Scientist jobs, the most frequently searched job titles are:

Infographic showing various Dft Scientist job openings in the United States as of September 2026, with employment types broken down into 2% Internship, 1% As Needed, 84% Full Time, 10% Part Time, and 3% Contract. Highlights an 81% Physical, 3% Hybrid, and 16% Remote job distribution, with an average salary of $94,420 per year, or $45.4 per hour.

Research Scientist, Quantum Chemistry

Cambridge, MA • On-site

Dayhoff Labs
See more jobs at Dayhoff Labsexternal

Full-time

Posted 25 days ago

Job description

About us
We're reverse-engineering the origin of life - one of the great unsolved problems in science, and one we think AI finally makes tractable. We believe that understanding this transition, from geochemistry to biochemistry, will let us orchestrate molecular networks and build systems that are more capable, adaptive, efficient, and intelligent.
If we succeed, the applications are vast: from catalysis and green synthesis to ab initio synthetic biology and programmable matter. Understanding and harnessing these processes could let ten billion of us thrive on this planet, and let us dream that diverse life keeps evolving and thriving beyond it.
We're a small, diverse team of AI engineers, computational scientists, and bench scientists. We hold ourselves to the rigor of a research institute, but we ship like an engineering firm. Global team, HQs in Cambridge, MA and London, UK.
The role
You'll turn quantum-chemistry calculations into kinetic datasets and mechanistic insight our ML models can actually train on. You'll study reaction mechanisms across homogeneous, heterogeneous, and enzymatic systems, and build the protocols that make that data reliable at scale.
What you'll do
  • Run DFT and post-HF calculations to study kinetics and mechanism, primarily in homogeneous catalysis
  • Build and benchmark reproducible protocols for kinetic data generation, with real uncertainty quantification
  • Design kinetic datasets for ML training and validation, and set data-quality standards with ML collaborators
  • Extend these methods systematically across catalytic systems and reaction conditions

Essential experience
  • PhD in computational or theoretical chemistry with a catalysis focus, and first-author papers on catalytic mechanisms
  • Fluency with a production quantum-chemistry package (Gaussian, ORCA, or similar)
  • Sound DFT judgment for transition-metal systems: functional choice, basis sets, dispersion corrections
  • Hands-on kinetics: transition-state location, IRC, rate constants, free-energy and thermodynamic analysis
  • Python and the computational-chemistry stack (ASE, cclib, RDKit)

Highly preferred
  • First-author work in homogeneous-catalysis kinetics
  • Heterogeneous (periodic DFT, surfaces, adsorption) or enzyme catalysis
  • Advanced methods for hard systems: DLPNO-CCSD(T), CASPT2, multireference approaches
  • High-throughput workflows, HPC, and automation
  • Uncertainty quantification and protocol benchmarking
  • Dataset design and prior collaboration with ML teams

Logistics
Compensation is highly competitive. We're also able to sponsor visas for the right candidate.

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