Founding ML Researcher/Engineeer

Machine Learning Researcher / Engineer

Location: New York, NY
Compensation: $300,000 - $400,000 Base Salary + Equity

The Opportunity

A highly ambitious computational science organization is building machine learning systems to address some of the most challenging problems in molecular science.

The team combines expertise across machine learning, physics, chemistry, biology, and high-performance computing to develop new computational approaches for understanding and predicting complex molecular behavior. Recent advances in machine learning have created opportunities to rethink how molecular systems are modeled, simulated, and optimized, and this organization is investing heavily in building the technical capabilities required to pursue that vision.

This role offers the opportunity to work at the intersection of frontier machine learning and fundamental science. You will collaborate with researchers from diverse technical backgrounds while contributing directly to the development of novel machine learning methods, scientific modeling approaches, and large-scale computational systems.

This is an environment designed for individuals who enjoy tackling difficult open-ended problems, publishing impactful research, and pushing the boundaries of what machine learning can achieve in scientific domains.

What You'll Work On

Machine Learning for Molecular Science

• Develop and apply state-of-the-art machine learning approaches to challenging problems in molecular modeling and scientific discovery.
• Build models that improve the understanding, prediction, and optimization of complex molecular systems.
• Explore novel architectures and learning paradigms for scientific machine learning applications.
• Design experiments to evaluate model performance and scientific utility.

Research & Algorithm Development

• Contribute to the development of new machine learning methods at the intersection of AI and physical sciences.
• Work on problems involving generative modeling, representation learning, geometric deep learning, and scientific foundation models.
• Collaborate closely with domain experts to translate scientific challenges into machine learning solutions.
• Publish and communicate research findings internally and externally when appropriate.

Large-Scale Computation

• Leverage modern deep learning frameworks such as PyTorch and related scientific computing tools.
• Train and evaluate models using large-scale computational resources.
• Develop efficient workflows for experimentation, benchmarking, and model iteration.
• Contribute to infrastructure and tooling that accelerates research productivity.

Basic Qualifications

• PhD in Machine Learning, Computer Science, Physics, Chemistry, Applied Mathematics, Computational Biology, or a related quantitative discipline.
• Strong expertise in deep learning and modern machine learning methodologies.
• Advanced Python programming skills.
• Hands-on experience with PyTorch and large-scale model development.
• Demonstrated ability to design, implement, and evaluate machine learning systems.
• Strong publication record or evidence of impactful research contributions.

Preferred Qualifications

Experience in one or more of the following areas:

• Scientific machine learning
• Molecular simulation
• Computational chemistry
• Structural biology
• Representation learning
• Geometric deep learning
• Graph neural networks
• Generative modeling
• Molecular foundation models
• Quantum chemistry
• High-performance computing
• Large-scale distributed training

Publication history in leading venues such as NeurIPS, ICML, ICLR, Nature, Science, Cell, JCTC, RECOMB, ISMB, or related conferences and journals is highly valued.

Why Join

This is an opportunity to work on genuinely difficult scientific problems alongside world-class researchers and engineers.

The organization is investing heavily in machine learning as a core scientific capability and is seeking individuals who want to help define how next-generation computational approaches can accelerate discovery across molecular science.