Scientist, Machine Learning (Principal Scientist - Associate Director)

Superluminal Medicines • Boston, MA

4d

About The Position

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. 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.

Requirements

• 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

Nice To Haves

• 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

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

Benefits

• Comprehensive benefits package that fully covers employees’ annual deductibles and monthly premiums for medical, dental, and vision insurance.
• 401(k) match program
• Massachusetts transportation subsidy
• Equity
• Unlimited paid time off
• Disability and life insurance

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Education Level: Ph.D. or professional degree