[Hiring] ML Researcher @Geolava

Role Description

We are looking for an ML Researcher to help build Geolava's world model for the built world: a system that perceives the current state of properties and infrastructure, learns how they change over time, and forecasts what comes next, whether the driver is time, a renovation, a new construction, or a hurricane. You will work from raw earth observation and property data all the way to a model that can reason about state, action, and future state, and you will decide how we measure whether it works.

This is a research role on a small team, and you will own real problems end to end:

• Framing the question
• Designing the experiment
• Running it on real data at scale
• Writing up what you learned

It is also a startup. Research that works here does not sit in a paper. It ships as a capability inside the product that property investors, lenders, and infrastructure owners use to make decisions.

Responsibilities (What You'll Do)

• Design, train, and evaluate world models of the built environment that predict how assets change over time and in response to interventions and external forces.
• Develop representation learning and predictive modeling approaches over multi-temporal aerial and satellite imagery, property records, and other physical and administrative signals.
• Build rigorous evaluation for a domain with no public benchmarks: baselines, held-out temporal splits, calibration, and tests that distinguish genuine forecasting skill from leakage.
• Run experiments on cloud GPUs with attention to throughput and cost, and turn results into clear, decision-ready writeups.
• Work with the AI and engineering teams to take successful research into production as capabilities in Geolava's platform.
• Stay current with the world-model, self-supervised learning, and generative modeling literature, and bring the ideas that matter into our work.

Qualifications

• World-model research experience.
• You have built or contributed to world models: learned dynamics models, latent predictive models, action-conditioned generation, or closely related work, in academia or industry. This is required.
• PhD in machine learning, computer vision, or a closely related field.
• Deep learning fluency.
• Empirical rigor.
• Ownership.
• Clear communication.

Requirements

• PhD in machine learning, computer vision, or a related quantitative field.
• Demonstrated research work on world models or learned dynamics models, with publications, open-source work, or shipped systems to show for it.
• Expert-level PyTorch and experience training and fine-tuning vision transformers or diffusion models at scale.
• Strong Python and data engineering skills for large image and tabular datasets.
• Experience running training workloads on cloud GPU infrastructure.
• Experience with remote sensing, geospatial data, or spatiotemporal modeling is a strong plus.

Benefits

• Be at the cutting edge of Physical AI.
• Competitive salary. Compensation varies according to candidate level and experience.
• Excellent benefits including medical, dental, and vision insurance, a retirement plan, short & long term disability and life insurance.