Globeholder.ai turns location into intelligence.

We build a planetary “physical reasoning” layer and universal geo-embeddings that give any coordinate a rich, omni-context—enabling teams to predict, optimize, and act in the real world with unprecedented precision.

Our mission is to bridge the gap between geospatial data and actionable insight by combining foundation models, AI-native APIs, and intelligent monitoring systems that help industries—from energy to agriculture to infrastructure—see and reason about the planet.

This is a full-time remote position.

As a Senior Data Scientist / Machine Learning Engineer with 6+ years of hands-on market experience, you will lead the design, validation, and deployment of advanced data science solutions and ML systems across diverse verticals.

You will take end-to-end ownership of key use cases—translating complex challenges into scalable, production-grade intelligence systems. The ideal candidate combines strong technical depth with creativity, autonomy, and an experimental mindset.

A crucial part of this role is working with agentic LLMs—you’ll design, evaluate, and operationalize systems that leverage RAG, Tool Calling, and MCP frameworks to bring intelligence and automation to geospatial reasoning.

• Design, build, and deploy ML models (supervised, unsupervised, time-series, and deep learning).
• Develop and productize LLM-based agentic architectures, integrating multi-modal reasoning and tool-using capabilities.
• Own the full data lifecycle: from data engineering and orchestration to model deployment and monitoring.
• Collaborate cross-functionally with product, engineering, and domain experts to deliver measurable impact.
• Contribute to MLOps pipelines, API-based inference systems, and cloud-native deployments.
• Translate ambiguous business questions into data-driven hypotheses, measurable experiments, and actionable insights.

• 6+ years of professional experience delivering data science or ML solutions to production.
• Deep expertise in machine learning and statistical modeling (supervised, unsupervised, time-series).
• Strong Python skills and fluency with modern ML tooling; advanced PyTorch modeling experience.
• Proven agentic LLM experience: RAG, Tool Calling, MCP.
• Hands-on geospatial data experience (raster, vector, satellite imagery, or spatial analytics).
• Strong data engineering fundamentals: ETL/ELT pipelines, orchestration tools, Spark or similar frameworks.
• Solid grounding in MLOps and software engineering: containerization, CI/CD, testing, model monitoring, and API integration; experience transforming research notebooks into production-grade services.
• Expertise in cloud platforms (AWS, GCP, or Azure).
• Excellent analytical thinking, product sense, and communication—able to bridge technical and business domains and tell a clear, evidence-based story.

• Shape the next generation of planetary-scale AI systems.
• Work with cutting-edge LLM and geospatial modeling technologies.
• Fully remote, globally collaborative team.
• A culture that values impact, autonomy, and creativity over bureaucracy.