Staff AI Researcher for Ecogical and Industrial Optimization

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 AI reasoning capabilities that enable stakeholders to target their mitigation efforts. We solve complex, global-scale problems by iteratively de-risking our technology, refining our tech prototypes, running experiments with partners and developing a valuable product for our users.

About the role

As an Applied AI Researcher, you will lead the creation of state-of-the-art multi-modal models for ecological, physical, and socioeconomic systems while designing algorithms for optimal system designs. This role requires a deep passion for problem-solving and experimentation, ability to work in diverse domains using a wide variety of machine learning techniques and passion for delivering real capabilities that power the team’s mission. Further, you will pioneer our ability to develop scientific and techno-economic models automatically to enable us to rapidly scale our capabilities to diverse and complex use-cases. As our research visionary you will also serve as a critical bridge between cutting-edge AI research and real-world enterprise applications.

How you will have 10X impact:

Accelerate the automated creation of scientific and techno-economic models, allowing for rapid expansion into a wide array of intricate use-cases. In this role, you will act as a visionary leader, bridging the gap between advanced AI research and practical enterprise deployments.

How you will make 10x impact:

• Drive innovation in modeling techniques across varied scientific and technoeconomic fields.
• Develop technologies to automate the generation of mechanistic, physics-inspired and operator models of various real-world phenomena.
• Create methodologies for large-scale, global designs utilizing these models.
• Utilize Earth Foundation Models and scalable geospatial tech to build models that detect natural phenomena and their temporal dynamics.
• Partner with scientists and engineers to convert research into production-grade solutions using GCP and MLOps, facilitating robust experimentation and client delivery.
• Engineer advanced agentic systems to interpret real-world data, produce task-specific algorithms, and support users in reaching climate goals.
• Keep pace with the rapid evolution of ML, specifically focusing on generative and multi-modal systems.
• Act like an owner; be fearless in diving deep, asking questions, proposing solutions, establishing consensus and then making things happen.

What you should have:

• • Graduate degree (Master's or PhD) in Engineering, Computer Science, or a comparable technical discipline.
• • 6+ years of professional experience in the design, development, and deployment of machine learning solutions.
• • Demonstrated proficiency in training deep learning, generic, and domain-specific mechanistic models (e.g., biological or physical).
• • Practical experience working with LLMs, Earth Foundation Models, and multi-modal systems like VLMs.
• • Strong Python programming abilities and a solid grasp of software engineering best practices.
• • Familiarity with MLOps/DevOps tools and methodologies, including Vertex AI, Kubernetes, and CI/CD pipelines.

It’d be great if you also had these:

• • Prior experience thriving in small-team or startup environments.
• • Background knowledge in climate science, ecosystem dynamics, or economics.
• • A record of published research in pertinent academic fields.

The US base salary range for this full-time position is $207,000 - $300,000 + 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.