About X

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 a small, passionate and driven team of experienced ML researchers and software engineers on a mission to tackle climate change by developing novel neural reasoning capabilities that enable stakeholders to target their mitigation efforts. Our moonshot is focusing on de-risking our technology quickly, refining our tech prototypes, running experiments with partners and developing a valuable product for our users. Our culture is one of mutual care and respect, individual competence and most important of all, fun!

 

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, while employing MLOps tools to manage the provenance of your data and your overall experimentation process.
• Develop and fine-tune sophisticated multi-modal neural and generative models using frameworks like PyTorch, libraries from the Hugging Face ecosystem and Foundation Models from the GCP Vertex ecosystem.
• Develop sophisticated agentic workflows that make inferences about the real world, generate algorithms for various tasks and interact with users to help them achieve their climate mitigation goals.
• Leverage Google Cloud Platform (GCP) and Vertex services to deploy and scale our ML models and infrastructure.
• Train and deploy geospatial models for detection of features and time series dynamics of natural phenomena, leveraging scalable geospatial infrastructures and Earth Foundation Models.
• Collaborate with cross-functional teams of domain scientists and software 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:

• PhD 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
• Proven experience building and training deep learning and generic models.
• Demonstrated experience with Large Language Models (LLMs), multi-modal models (e.g., Vision Language Models (VLMs)) and Earth Foundation Models.
• Strong proficiency with Google Cloud Platform (GCP) and the Vertex 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).
• Experience in start-up or small team environments

 

It’d be great if you also had these:

• A foundational knowledge of climate system processes, the dynamics of ecosystems and/or economics.
• Published research papers in relevant fields.

 

The US base salary range for this full-time position is $165,000 - $280,000 + 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.