X's AI for chemistry moonshot is applying AI to supercharge processes related to the manufacturing of existing chemical compounds.
Requirements
- Proven experience building and training deep learning models using PyTorch and the Hugging Face library
- Demonstrated experience with Large Language Models (LLMs), multi-modal models (e.g., Vision Language Models (VLMs)) and computer vision models (eg. OCR)
- Strong proficiency with Google Cloud Platform (GCP) and its core services for machine learning and data processing.
- Excellent programming skills in Python and a solid understanding of software engineering best practices.
- Hands-on experience with MLOps principles and tools (e.g., Kubeflow, MLflow, Vertex AI Pipelines) and DevOps practices (e.g., CI/CD, Docker, Kubernetes, Terraform).
- Experience with large-scale data processing and database management (e.g., PostgreSQL, SQLAlchemy).
Responsibilities
- Design and build end-to-end machine learning pipelines for training, evaluation, and deployment.
- Develop and fine-tune sophisticated multi-modal models using frameworks like PyTorch and libraries from the Hugging Face ecosystem.
- Leverage Google Cloud Platform (GCP) services to deploy and scale our ML models and infrastructure.
- Collaborate with cross-functional teams of scientists and engineers to translate research ideas into production-ready solutions.
- Stay current with the latest advancements in machine learning, particularly in multi-modal learning and generative models.
Other
- Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.
- At least 5 years of experience designing, building and deploying ML solutions
- Experience in start-up or small team environments
- A foundational knowledge of organic chemistry concepts or experience working with chemical data (e.g., SMILES strings, molecular graphs).
- Published research papers in relevant fields
