Sr. Scientist / Principal Scientist, Computational Chemistry

Superluminal Medicines, Inc.; Boston (MA); On-site; USD 150,000 - 210,000; Full time

Benefits offered by this job

401(k) match

Massachusetts transportation subsidy

Equity

Unlimited PTO

Disability and life insurance

Job summary

Superluminal Medicines is seeking a Senior/Principal Scientist in Computational Chemistry to drive design and tool development for small molecule discovery. You will combine physics-based modeling, ML predictions, and structural biology with medicinal chemistry input to accelerate programs.

Join a team delivering iterative Computational Chemistry support, workflows, and cross-disciplinary collaboration in a fast-moving biotech environment focused on candidate optimization and program milestones.

Qualifications

• PhD in Computational Chemistry, Biophysics, or related field.
• 1–3+ years in biotech/pharma supporting small molecule discovery.
• Strong knowledge of physics-based and ML chemistry tools.
• Excellent ability to communicate design rationale to diverse scientists.
• Experience designing synthesizable compounds aligned with goals.

Responsibilities

• Integrate physics-based simulations with ML predictions to prioritize compounds for synthesis.
• Collaborate with interdisciplinary scientists to develop hypotheses and experiments.
• Design and prioritize chemical matter to hit program milestones (POC, ADMET, selectivity).
• Develop, validate and deploy CDTM workflows for Design-Make-Test-Analyze cycles.

Skills

Physics-based modeling

Machine learning

Structure-based design

Python scripting

Linux

Cloud computing

RDKit

Schrodinger suite

OpenEye

MOE

FEP

PyTorch

Education

PhD in Computational Chemistry or Biophysics

Tools

Schrodinger suite

OpenEye

MOE

RDKit

scikit-learn

NumPy

Pandas

PyTorch

Job description

Sr. Scientist / Principal Scientist, Computational Chemistry

About Superluminal Medicines:

Superluminal Medicines is a generative biology and chemistry company revolutionizing the speed and accuracy of how small molecule medicines are created. The Company’s platform aims to create candidate-ready compounds with unprecedented speed using a combination of deep biology, computational and medicinal chemistry, machine learning, and proprietary big data infrastructure. We are expanding the team of talented scientists who seek to build the future of small molecule drug discovery with creativity and innovation.

About the Role:

We are seeking a high-impact Computational Chemist to join our integrated discovery team. In this role, you will be the computational engine of our programs, combining physics-based modeling, machine learning and structural biology to generate the quantitative predictions and develop necessary workflows to drive small molecule drug discovery. You will serve as a core strategic partner to medicinal chemists and biologists, focusing on compound design and tool development to impact discovery pipeline and address unmet computational needs.

Key Responsibilities:

• Integrate physics-based simulations with ML predictions to achieve the quantitative accuracy required to prioritize compounds for synthesis
• Collaborate with a team of interdisciplinary scientists to develop actionable hypotheses and design computational experiments
• Design and prioritize chemical matter specifically aimed at hitting key program milestones, such as establishing in vivo POC, achieving selectivity windows, or optimizing ADMET profiles for candidate selection
• Develop, validate and deploy computational workflows to optimize the "Design-Make-Test-Analyze" cycles and address gaps

Required Qualifications:

• Ph.D. in Computational Chemistry, Biophysics, or a related field
• 1-3+ years of experience in a biotech or pharma setting performing computational support for small molecule drug discovery
• Advanced knowledge of physics-based and ML computational chemistry packages including knowing when and how to deploy various tools for maximum project impact
• Exceptional ability to communicate the "why" behind a design to a diverse scientific audience
• Design experience working in concert with medicinal chemistry teams to design synthesizable compounds that efficiently work towards defined goals of activity, affinity, selectivity, properties, etc
• A proven track record for innovation in structure-based small molecule drug discovery including developing and validating new workflows and techniques or expansions of existing ones

Preferred Qualifications:

• Experience working with structural biology teams to extract the most information possible from cryo-EM and x-ray crystallography experiments and using this to accelerate programs using structure-based drug discovery techniques
• Proven experience using ML to scale physics-based insights, specifically in the context of large-scale virtual screening or FEP-guided lead optimization
• A proven track record for innovation in structure-based small molecule drug discovery including developing and validating new workflows and techniques or expansions of existing ones
• Expert level use of structure-based small molecule drug discovery software tools including protein preparation, docking, FEP, QM, conformer selection. (Schrodinger suite, OpenEye, MOE, etc)
• Ability to work directly in a Linux-based environment
• Familiarity with cloud computing infrastructure (AWS, GCS) is a plus
• Python scripting and prototyping experience including knowledge of key packages (RDKit, scikit-learn, numpy, pandas, pytorch, etc)

Superluminal offers a comprehensive benefits package that fully covers employees’ annual deductibles and monthly premiums for medical, dental, and vision insurance. The package also includes a 401(k) match program, a Massachusetts transportation subsidy, equity, unlimited paid time off, and both disability and life insurance.

Equal Opportunity Statement:

Superluminal Medicines is an Equal Opportunity Employer committed to a culturally diverse workforce. All qualified applicants will receive consideration for employment without regard to race; color; creed; religion; national origin; age; ancestry; nationality; marital, domestic partnership or civil union status; sex, gender, gender identity or expression; affectional or sexual orientation; disability; veteran or military status or liability for military status.