Machine Learning Engineer, AI for Chemistry project

About X, the Moonshot Factory

X is a diverse group of inventors and entrepreneurs who build and launch technologies that aim to improve the lives of millions, even billions, of people. Our goal: 10x impact on the world’s most intractable problems, not just 10% improvement. We approach projects that have the aspiration and riskiness of research with the speed and ambition of a startup.

About the team

We’re X’s AI for chemistry moonshot, applying AI to supercharge processes related to the manufacturing of existing chemical compounds. We’re an agile team of experienced AI researchers, software engineers, and entrepreneurs working with partners and customers around the world. We’re looking for people who are passionate about working at the intersection of AI and science. You’ll be part of a fast-paced team that has the agility and impact of an early stage company, while building on world-class Google AI technology.

About the role

As a Machine Learning Engineer, you will be responsible for designing, developing, and deploying state-of-the-art multi-modal models. This role requires a deep passion for problem-solving and experimentation, moving across the full range of applied machine learning tasks from initial prototyping to building full-scale, robust solutions. While you will primarily focus on core product development, you will also serve as a critical bridge between cutting-edge AI research and real-world enterprise applications. You will be a key player in our mission to accelerate scientific discovery through machine learning.

How you will have 10X impact:

• 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.

What you should have:

• Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.
• A foundational knowledge of organic chemistry concepts or experience working with chemical data (e.g., SMILES strings, molecular graphs).
• 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)
• Knowledge of 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.
• Experience with large-scale data processing and database management (e.g., PostgreSQL, SQLAlchemy).
• Experience in start-up or small team environments

It’d be great if you also had these:

• Published research papers in relevant fields
• 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).

The US base salary range for this full-time position is $118,000 - $162,000 + bonus + equity + benefits. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits.