AI Engineer I — Drug Discovery & Multimodal ML

Frontier Medicines

South San Francisco (CA)

Vor Ort

USD 100.000 - 159.000

Vollzeit

14 Tage+

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Benefits dieser Stelle

Health Care Plan (Medical, Dental &amp
Retirement Plan (401k, IRA)
Life Insurance (Basic, Voluntary &
Paid Time Off (Vacation, Sick & P|
Family Leave (Maternity, Paternity)
Short Term & Long Term Disability
Training & Development
Free Food & Snacks
Wellness Resources
Stock Option Plan

Zusammenfassung

Frontier Medicines is seeking a highly motivated Scientist, AI Engineer to join our AI organization in South San Francisco. You will design architectures for domain-specific multimodal AI models, build multi-agent systems for scientific workflows, and deploy models to support covalent small molecule drug discovery.

You will work with medicinal chemistry, biology, and cross-functional teams to translate complex data into actionable insights, leveraging proprietary data to accelerate compound

Qualifikationen

  • Bachelor’s degree in computational or quantitative discipline.
  • Strong programming skills in Python.
  • Excellent written and verbal communication.
  • Authorized to work in the United States.

Aufgaben

  • Design architecture and training strategies for multimodal AI models.
  • Build multi-agent systems for scientific workflows and tool calling.
  • Develop generative molecular design approaches and SAR analysis.
  • Deploy models into serving infrastructure for researchers.

Kenntnisse

Python
Cross-functional collaboration
Data science

Ausbildung

Bachelor’s degree in computational/quantitative field
PhD with 1 year experience

Tools

DeepSpeed
FSDP
GNNs

Jobbeschreibung

Frontier Medicines is seeking a highly motivated Scientist, AI Engineer to join our AI organization in South San Francisco. You will design architectures for domain-specific multimodal AI models, build multi-agent systems for scientific workflows, and deploy models to support covalent small molecule drug discovery.

You will work with medicinal chemistry, biology, and cross-functional teams to translate complex data into actionable insights, leveraging proprietary data to accelerate compound

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