Principal Scientist – Machine Learning
Superluminal Medicines Inc.
full-time
Posted on:
Location Type: Hybrid
Location: Boston • Massachusetts • United States
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Job Level
Tech Stack
About the role
- 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
- ML lead on project teams, collaborating intimately with medicinal chemists to refine SAR and with structural biologists to integrate co-folding and structure-based insights into ML workflows
- Data-Driven Decision Making: 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.
Requirements
- Ph.D. in Computational Chemistry, Computer Science, Machine Learning, or a related field
- Demonstrated expertise in statistics, probability theory, data modeling, machine learning algorithms, and the languages used to implement analytics solutions
- 4-7+ years of experience in a biotech or pharma setting performing ML support for small molecule drug discovery with clear evidence of impact on drug discovery programs
- 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 led to the achievement of program milestones
- Expert proficiency in Python and deep learning libraries (e.g., PyTorch, TensorFlow) is required. You must be able to build and maintain production-quality code and data pipelines.
Benefits
- Health insurance
- Professional development opportunities
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
Large Language Modelsco-folding algorithmsgenerative chemistry techniquesstatisticsprobability theorydata modelingmachine learning algorithmsPythondeep learning librariesproduction-quality code
Soft Skills
data-driven decision makingcollaborationcross-functional teamworkmanagementdevelopment of team members
Certifications
Ph.D. in Computational ChemistryPh.D. in Computer SciencePh.D. in Machine Learning