EPM Scientific
Machine Learning Researcher – Generative Modeling for Drug Discovery
A talent-dense AI4Biology team is applying frontier-scale AI to model biological systems and accelerate therapeutic development. They are building novel architectures and generative frameworks to reason about molecular behavior, predict drug toxicity and potency, and simulate complex biological interactions.
This role is suited for a thoughtful and curious scientist with deep engineering rigor, focused on designing and training models that understand and generate molecular structure, dynamics, and function. It’s a zero-to-one opportunity to shape the technical foundation of a company working at the intersection of AI and human health.
Focus Areas
Designing generative models (e.g. diffusion, flow matching, graph-based) for molecular structure and dynamics
Building predictive systems for ADMET properties, drug-likeness, and off‑target effects
Developing simulation‑informed architectures that integrate molecular dynamics and physical priors
Engineering scalable training pipelines across multi‑cloud and distributed infra
Collaborating with domain experts in chemistry, biology, and pharmacology to ground models in real‑world constraints
Prototyping and evaluating novel architectures for reasoning over multimodal biological data
Ideal Background
Experience building or publishing on generative models for molecules, proteins, or materials
Familiarity with molecular dynamics simulations or physics‑informed ML
Expertise in graph neural networks, transformers, or diffusion models
Track record of shipping ML systems in production or research environments
Interest in model interpretability, uncertainty, and scientific grounding
Comfort working across disciplines and translating abstract scientific goals into concrete ML systems
Bonus
Experience with ADMET modeling, QSAR, or cheminformatics
Familiarity with simulation engines (e.g. OpenMM, GROMACS) or quantum chemistry tools
Contributions to open‑source ML or scientific computing projects
Candidates from
any
biological or scientific background are encouraged to apply. No specific domain (e.g., ADMET, chemistry, etc.) is required. Intellectual honesty, engineering excellence, and scientific curiosity are valued above domain specialization.
Seniority level
Mid‑Senior level
Employment type
Full‑time
Job function
Research and Science
Industries
Research Services
Biotechnology Research
Pharmaceutical Manufacturing
#J-18808-Ljbffr
This role is suited for a thoughtful and curious scientist with deep engineering rigor, focused on designing and training models that understand and generate molecular structure, dynamics, and function. It’s a zero-to-one opportunity to shape the technical foundation of a company working at the intersection of AI and human health.
Focus Areas
Designing generative models (e.g. diffusion, flow matching, graph-based) for molecular structure and dynamics
Building predictive systems for ADMET properties, drug-likeness, and off‑target effects
Developing simulation‑informed architectures that integrate molecular dynamics and physical priors
Engineering scalable training pipelines across multi‑cloud and distributed infra
Collaborating with domain experts in chemistry, biology, and pharmacology to ground models in real‑world constraints
Prototyping and evaluating novel architectures for reasoning over multimodal biological data
Ideal Background
Experience building or publishing on generative models for molecules, proteins, or materials
Familiarity with molecular dynamics simulations or physics‑informed ML
Expertise in graph neural networks, transformers, or diffusion models
Track record of shipping ML systems in production or research environments
Interest in model interpretability, uncertainty, and scientific grounding
Comfort working across disciplines and translating abstract scientific goals into concrete ML systems
Bonus
Experience with ADMET modeling, QSAR, or cheminformatics
Familiarity with simulation engines (e.g. OpenMM, GROMACS) or quantum chemistry tools
Contributions to open‑source ML or scientific computing projects
Candidates from
any
biological or scientific background are encouraged to apply. No specific domain (e.g., ADMET, chemistry, etc.) is required. Intellectual honesty, engineering excellence, and scientific curiosity are valued above domain specialization.
Seniority level
Mid‑Senior level
Employment type
Full‑time
Job function
Research and Science
Industries
Research Services
Biotechnology Research
Pharmaceutical Manufacturing
#J-18808-Ljbffr