Impress employers and recruiters.
Choose from hundreds of resume examples.

Target Resume Now

Tailor your resume to this Senior Scientist, Computational Receptor Biology & Machine Learning - Long Island City, NY role.

Rezi rewrites your resume against dsm-firmenich's job description. Free.

Resume score gauge reading 58 out of 100

Don't guess if your resume is good enough.

See how it scores against the Senior Scientist, Computational Receptor Biology & Machine Learning - Long Island City, NY posting at dsm-firmenich — free, in seconds.

About the Role

Join us at the intersection of AI, biology, and sensory science. We are seeking an exceptional Machine Learning Researcher with deep expertise in deep learning architectures and distributed training systems to help push the boundaries of protein-ligand interaction modeling. As part of our Data Science Computational Receptor Biology team, you’ll collaborate with world-class data scientists, chemists, and biologists to decode the molecular mechanisms behind taste and smell. Working at the forefront of protein and molecular AI, you’ll develop next-generation models that accelerate scientific discovery and unlock innovative solutions in nutrition, health, and beauty. The Senior Scientist, Computational Receptor Biology & Machine Learning position is a unique opportunity to apply state-of-the-art machine learning techniques to complex biological challenges, contributing to groundbreaking research with real-world impact. You’ll play a key role in shaping interdisciplinary projects that combine advances in computational biology, large-scale deep learning, and receptor science while partnering with leading experts across scientific domains.

Responsibilities

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

Requirements

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

Skills

Machine Learning
Deep Learning
Deep Learning Architectures
Distributed Training Systems
Protein-Ligand Interaction Modeling
Geometry
Physics
Probabilistic Reasoning
Equivariant Architectures
Multimodal Architectures
Generative Architectures
Diffusion Models
Flow-based Models
Structured Molecular Data
Biological Data
Domain-Specific Representations
Domain-Specific Constraints
Scalable Model Training
GPU Training
Model Evaluation
Model Iteration
Reproducibility
Receptor-Aware Modeling
PyTorch
JAX
Generative Modeling
Probabilistic Modeling
Score-Based Models
Uncertainty Estimation

Location

Long Island City, NY
San Diego, CA

Work Type

Full-time

Experience Level

Senior
3-7 years of additional academic or industry experience

Education Level

Ph.D. or equivalent experience

Salary/Compensations

$132,000-$158,000

Benefits

Annual incentive pay
Retirement savings plan
Health care coverage
Paid time off
Recognition programs
Comprehensive total rewards package
In-house training courses

About the Company

At dsm-firmenich, we don’t just meet expectations – we go beyond them.
Join our global team powered by science, creativity, and a shared purpose: to bring progress to life.
From elevating health to making fortified food and sustainable skincare, the impact of your work here will be felt by millions – every single day.
Whether it’s fragrance that helps you focus, alternative meat that’s better for the planet, or reducing sugar without losing flavor, this is where you help shape the future of nutrition, health, and beauty for everyone, everywhere.
And while you’re making a difference, we’ll make sure you’re growing too.
With learning that never stops, a culture that lifts you up and the freedom to move across businesses, teams, and borders.
Your voice matters here. And your ideas? They’re essential to our future.
Because real progress only happens when we go beyond, together.
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

Equal Opportunity

At dsm-firmenich, we believe being a force for good starts with the way we treat each other.
When people feel supported, included, and free to be themselves, they do their best work – and that’s exactly the kind of culture we’re building.
A place where opportunity is truly equal, authenticity is celebrated, and everyone has the chance to grow, contribute, and feel they belong.
We’re proud to be an equal opportunity employer, and we’re serious about making our hiring process as fair and inclusive as possible.
From inclusive language and diverse interview panels to thoughtful sourcing, we’re committed to reflecting the world we serve.
We welcome candidates from all backgrounds — no matter your gender, ethnicity, sexual orientation, or anything else that makes you, you.
And if you have a disability or need any support through the application process, we’re here to help – just let us know what you need, and we’ll do everything we can to make it work.