Scientist, Machine Learning (Principal Scientist - Associate Director)

Superluminalrx; Boston (MA); On-site; USD 90,000 - 120,000; Full time

Benefits offered by this job

401(k) match

Massachusetts transportation subsidy

Unlimited paid time off

Medical, dental, and vision insurance

Life insurance and disability coverage

Job summary

Superluminalrx is seeking a Machine Learning Scientist to advance small molecule drug discovery using applied ML. The ideal candidate will enable the development and validation of ML models, collaborating with chemists to refine design hypotheses and drive program milestones.

This role requires a Ph.D. in a relevant field, 2+ years of experience, and proficiency in Python, along with strong skills in ML frameworks. A comprehensive benefits package including medical insurance, 401(k) match, and unlimited paid time off is offered.

Qualifications

• Ph.D. in Computational Chemistry, Computer Science, or a related field.
• 2+ years applying ML methods in drug discovery programs.
• Strong practical proficiency in Python and deep learning libraries.
• Demonstrated expertise in statistics and data modeling.
• Experience in a cross-functional team environment.

Responsibilities

• Lead ML applications for drug discovery and decision-making.
• Collaborate with chemists to refine modeling efforts.
• Synthesize ML outputs into actionable design hypotheses.
• May manage and develop internal team members.

Skills

Machine Learning

Python

Deep Learning Libraries

Statistical Analysis

Data Modeling

Interpersonal Communication

Education

Ph.D. in Computational Chemistry or related field

Tools

PyTorch

TensorFlow

RDKit

scikit-learn

Job description

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 Machine Learning Scientist to join our integrated discovery team and help advance small molecule drug discovery programs through applied ML. In this role, leading from the bench, you will enable the development, validation and deployment of state-of-the-art ML models to generate the quantitative predictions necessary to drive drug discovery. Beyond technical mastery, you will serve as a core strategic partner to medicinal chemists, computational chemists, and biologists, building models that move programs efficiently toward program decision points and candidate nomination.

Key Responsibilities

• Lead the application of Large Language Models (LLMs), co-folding algorithms, and generative chemistry techniques to design novel chemical matter aimed at hitting key program milestones, such as establishing selectivity windows and optimizing drug‑like properties
• Serve as the machine learning POC on cross‑functional projects partnering with medicinal chemists and structural biologists to refine SAR and structure-informed modeling efforts
• Synthesize complex ML outputs into clear, actionable design hypotheses that cross‑functional scientific stakeholders can use to make high‑stakes program decisions
• May be responsible for management and development of internal team members

Required Qualifications

• Ph.D. in Computational Chemistry, Computer Science, Machine Learning, or a related field
• 2+ years applying ML methods in a small molecule drug discovery programs in biotech or pharma environments
• Demonstrated expertise in statistics, probability theory, data modeling, machine learning algorithms, and the languages used to implement analytics solutions
• Demonstrated success in a cross‑functional environment, including biologists, structural biologists, medicinal and computational chemists, with specific examples of computational designs/algorithms/models that directly influence achievement of program milestones
• Strong practical proficiency in Python and deep learning libraries (e.g., PyTorch, TensorFlow) is required. Demonstrated ability to build and maintain robust, production-quality ML code and data workflows

Preferred Qualifications

• Proven experience with protein‑ligand co‑folding models (e.g.,Boltz, OpenFold, AlphaFold, etc) and the ability to integrate these structural insights into broader ML discovery pipelines
• Expertise fine‑tuning existing models with internally generated structural biology and biology data
• Strong knowledge of deep learning frameworks, specifically for affinity prediction, ADMET modeling, and the application of LLMs in a biological or chemical context
• Experience mentoring and developing teams

Skills & Competencies

• A demonstrated track record of innovation in the ML/AI space, including developing and validating new architectures or novel applications of existing models to solve complex drug discovery problems
• Demonstrated expertise using small molecule drug discovery ML/AI tools e.g. AlphaFold, Boltz, OpenFold, ChemProp, DeepChem, Reinvent, etc)
• Strong level coding for ML tasks including knowledge of key packages (RDKit, scikit-learn, numpy, pandas, pytorch, DeepChem, polars, PyG/DGL).
• Strong interpersonal and communications skills in the "why" behind a design to a diverse scientific audience

Benefits

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.