Senior Scientist, Computational Receptor Biology & Machine Learning - Long Island City, NY
$132,000-$158,000
ApplySenior Scientist, Computational Receptor Biology & Machine Learning - Long Island City, NY
Posted 12 days ago
SENIORITY
Senior
SALARY
$132,000-$158,000
About the role
- Lead development of advanced deep learning models grounded in geometry, physics, and probabilistic reasoning, and apply them to receptor–ligand interaction problems
- Design and adapt equivariant, multimodal, and/or generative architectures (e.g., diffusion, flows) for structured molecular and biological data, with openness to learning domain-specific representations and constraints
- Own scalable model training distributed across Graphics Processing Units (GPUs), evaluation, and iteration workflows, ensuring rigor, reproducibility, and measurable impact in discovery settings
- Translate foundational machine learning (ML) ideas into practical receptor-aware modeling tools, collaborating closely with domain experts to bridge theory and application
- Contribute technical leadership and mentorship while working cross-functionally with experimentalists, physicists, chemists, and ingredient modeling partners
- We Bring:
- Opportunity to join a dynamic and thriving team applying machine learning and physics-based modeling to biology and chemistry
- Highly motivated, professional and committed multicultural and interdisciplinary team
- Opportunity to put your scientific skills into practice with innovations in health, nutrition and beauty
- Chance to grow and develop your skills through our in-house training courses
- Be a part of company shaping a strong legacy heritage through industrial innovations and cutting-edge technology
- A commitment to science-based innovations with 2,000 scientists and large annual Science & Research investments
- You Bring:
- Ph.D. or equivalent experience in Computer Science, Mathematics, Physics, Computational Biology or a related field 3-7 years of additional academic or industry experience developing novel architectures or training paradigms
- Deep expertise in ML, applied mathematics, or physics-inspired modeling, with strong intuition for geometry, symmetry, probabilistic inference, and learning dynamics
- Hands-on experience with modern deep learning frameworks (PyTorch and/or JAX) and ability to build non-trivial models for structured or geometric data
- Familiarity with generative or probabilistic modeling approaches (e.g., diffusion, flows, score-based models, uncertainty estimation), even if applied outside biomolecular systems
- A demonstrated ability and enthusiasm to learn new scientific domains quickly and collaborate with subject-matter experts to apply ML in real discovery contexts
Before you apply
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Your profile is current
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Two examples you can talk through
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A number in mind
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Your notice period
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Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
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