Research Scientist; AI​/Machine Learning

Opportunity

I am currently partnering with a global, R&D-driven pharmaceutical powerhouse that is reinventing drug discovery through a massive investment in Artificial Intelligence. My client is moving beyond isolated ML applications to build a fully integrated, AI-first discovery engine.

In this role, you won’t just be applying off‑the‑shelf tools; you will be pre‑training and fine‑tuning foundational models (LLMs, Diffusion, Multi‑modal) on massive, proprietary scientific datasets. Your goal is to create a unified intelligence that understands everything from protein 3D structures and omics data to medicinal chemistry and biomedical imaging.

• Architect Foundational Models:
  Lead the development of generative models (Diffusion, Flow‑matching, Transformers) designed specifically for molecular design and protein engineering.
• Multimodal Integration:
  Build architectures capable of synthesizing diverse data types, including transcriptomics, proteomics, and structural biology.
• Scientific Grounding:
  Partner with biologists and “drug hunters” to ensure AI outputs are biologically viable and accelerate the path to the clinic.
• Scaling & Deployment:
  Utilize GPU clusters and cloud environments (AWS/GCP) to train models from scratch on massive scientific corpora.
• Thought Leadership:
  Stay at the cutting edge of GenAI, contributing to top‑tier publications and internal innovation.

• Advanced Degree:
  PhD in Computer Science, ML, Computational Biology, or a related field (or equivalent high‑level industry experience).
• Deep Learning Mastery:
  Proven expertise in PyTorch and experience training large‑scale models (Transformers, GNNs, or Diffusion).
• Domain Literacy: A solid understanding of biology or chemistry is essential—you must be able to speak the language of the scientists your models will support.
• Technical Stack:
  Proficiency in Python, distributed training frameworks, and cloud‑based GPU scaling.
• Specific
  
  Experience:
  
  Familiarity with Protein Language Models (e.g., ESM) or molecular generative frameworks.

• A track record of publications at major conferences like NeurIPS, ICML, or ICLR.
• Experience with model compression and efficient inference for production‑grade AI.
• Background in 3D molecular representations or knowledge graphs.

My client offers a world‑class R&D environment where your work directly impacts patient lives. You will have access to some of the richest biological datasets in the industry and the computational resources to match. The package includes a competitive base salary, short‑term and long‑term incentives, and a robust benefits suite (401k match, tuition reimbursement, and generous PTO).