We believe quantum computing will change everything.  It will have the power to transform the foundations of giant industries that rely on intensive computation.  PsiQuantum is on a mission to build the world’s first useful quantum computer, capable of delivering these transformative results.  We know that means it will need 1 million qubits, error correction, a scalable architecture and a data center footprint.

By harnessing the laws of quantum physics, quantum computers can provide exponential performance increases over today’s most powerful supercomputers, offering the potential for extraordinary advances across a broad range of industries including climate, energy, healthcare, pharmaceuticals, finance, agriculture, transportation, materials design, and many more. 

PsiQuantum is uniquely positioned to deliver on the promise of quantum computing years earlier than our competitors. Our architecture is based on photonics which gives us the ability to produce our components using existing high-volume semiconductor manufacturing processes, the same processes that are today producing billions of chips for telecom and consumer electronics applications. Since photons don’t feel heat, we can take advantage of existing cryogenic cooling systems, the kind that have been operating at facilities like the Stanford Linear Accelerator (SLAC).  And because photons are in fact light, they can move between subsystems at tremendous speed using standard fiber connectivity.

Our team is building a utility scale quantum computer and the software tools needed to build fault tolerant quantum applications. We’re a highly motivated and collaborative group focused on a singular goal - building the world’s first useful quantum computer on the fastest path possible. 

There’s much more work to be done and we are looking for exceptional talent to join us on this extraordinary journey! 

Job Summary:

Are you passionate about working at the intersection of machine learning, computational chemistry, drug design, and materials science to maximize the impact of quantum algorithms and fault tolerant quantum computing (FTQC) in generating exact and useful quantum data for a wide range of industrial applications? As a Quantum Solutions Cheminformatics Scientist, you will join a dynamic and rapidly expanding team of theoretical physicists/chemists, machine learning experts, and quantum information scientists, which values individuals with a diverse set of skills. You will join a supportive environment that thrives on innovation and collaboration, offering opportunities for personal growth and leadership. This role will hone your skills as a problem solver and solution maker, keeping you at the forefront of cheminformatics, applied machine learning, and molecular modeling while working within a leading company on track to building the world’s first useful quantum computers.

At PsiQuantum’s Quantum Solutions team, your role will focus on bridging the gap between fault-tolerant quantum computing (FTQC) and established cheminformatics and computational physics and chemistry tools, to pioneer quantum computer-aided materials, chemicals, and drug design. You will contribute expert knowledge in applied machine learning and computational and theoretical (materials) chemistry simulations and engage in collaborative problem-solving to link atomistic quantum-computed properties with complex macroscopic observables. These efforts will support innovations in applied fields such as drug design, energy materials, batteries, and catalysis, among others.

This position is ideal for a PhD holder in computational chemistry, physics, or materials science (or a closely related field), preferably with postdoctoral research experience (although postdoc experience is not mandatory). We are looking for a curious, creative, and interdisciplinary thinker with a broad understanding of various machine learning, chem- and materials-informatics methodologies. The ideal candidate should be an avid reader of scientific literature, have hands-on coding experience (e.g., Python), and possess skills in applying machine learning and molecular simulations to solving real-world problems. While prior knowledge of quantum information and fault-tolerant quantum computing is a plus, it is not required. More importantly, you should be a curious person with a keen interest in and dedication to learning about these fields as needed.

Responsibilities:

1) Conduct innovative research, literature analysis, problem solving, and quantum workflow design in the areas of quantum-informed multiscale modeling, cheminformatics, materials informatics, and computational physics and chemistry.
(2) Collaborate with quantum algorithm experts where quantum computing can be deployed with greatest impact in cheminformatics, specifically drug design and computational chemistry and materials problems.
(3) Contributing non-quantum-computing algorithm expertise to the development of in-house quantum algorithms. Serve as a technical lead in customer projects by collaborating with customers’ teams to integrate quantum computing-produced computational outputs into conventional customer workflows.
(4) Act as an interface between partner teams and PsiQuantum quantum information experts for innovative algorithm selection, development, prioritization, and deployment.
(5) Develop computational workflows that combine best-in-class conventional approaches (e.g., through high performance computing) with the breakthrough computational abilities of FTQC to reshape how quantum (materials) chemistry workflows are designed.
(6) Serve as a cheminformatics, machine learning, and molecular modeling source of knowledge to PsiQuantum’s Quantum Solutions team, keeping aware of recent academic literature, trends, and tools.
(7) Build strong cross-team relationships within PsiQuantum to enable innovative use of quantum chemistry algorithms to achieve maximum impact for quantum computing-generated atomistic chemistry data.
(8) Help shape external-facing materials that champion the applications of FTQC in relevant industries (for key opinion leaders, media, and partners).
(9) Build and maintain external partnerships and collaborations on the topic which can include meetings, group problem solving sessions, or drafting research proposals, among others.
(10) Create organized internal reports and well document progress made for directions above.

Experience/Qualifications:

Required:
• Ph.D. in computational (theoretical) chemistry, physics, materials science, or a closely related field with a strong focus on applied machine learning-based research.
• Strong foundational knowledge in density functional theory (DFT) and/or advanced quantum mechanical wavefunction-based methods, including coupled clusters and configuration interaction methods.
• Deep knowledge of machine learning and molecular quantum chemistry.
• Enthusiasm for working in a collaborative, interdisciplinary, and dynamic team environment.
• Published track record including (more than one) first-author papers in the field of cheminformatics combined with molecular modeling and applied machine learning (e.g., development of machine learning interatomic potentials, chemical descriptor engineering, and QSAR-type descriptor-based modeling).
• Comfortable and experienced with coding in Python.

Highly Preferred:
• Methodology development experience in quantum chemistry.
• Experience working with multiple python-based quantum chemistry and molecular modeling packages, e.g., PySCF, ASE, GPAW, and pymatgen.
• Deep expertise in working with machine learning potentials applied to molecular dynamics.
• Knowledge of active learning applied to chemical and materials simulations.
• Familiarity with quantum mechanics/molecular mechanics (QM/MM) methodologies.
• Work experience with QSAR-type modeling.
• Work experience with computational drug design pipeline including molecular docking, and FEP calculations.

Preferred:
• Postdoctoral experience.
• Deep knowledge of both condensed matter physics and molecular chemistry.
• Experience with GenAI.
• Familiarity with free energy calculations.
• Experience with density matrix renormalization group (DMRG) method.
• Experience with quantum embedding theories, e.g., DMET.
• Experience with quantum dynamics, e.g., RT-TDDFT method.
• Knowledge of quantum computing principles and algorithms.
• Classical forcefield training experience.
• Familiarity with coarse grained molecular dynamics.

PsiQuantum provides equal employment opportunity for all applicants and employees. PsiQuantum does not unlawfully discriminate on the basis of race, color, religion, sex (including pregnancy, childbirth, or related medical conditions), gender identity, gender expression, national origin, ancestry, citizenship, age, physical or mental disability, military or veteran status, marital status, domestic partner status, sexual orientation, genetic information, or any other basis protected by applicable laws.

Note: PsiQuantum will only reach out to you using an official PsiQuantum email address and will never ask you for bank account information as part of the interview process. Please report any suspicious activity to [email protected].

We are not accepting unsolicited resumes from employment agencies.