Head of Weather Long , California,

At Mantari Industries , we’re replacing yesterday’s public sector weather systems with a full stack, AI-native platform that senses, models, and predicts Earth’s atmosphere and oceans in near real time. Our mission is to sense Earth in its entirety — oceans, skies, and space — and close the gap between what nature does and what humanity knows. By building a distributed network of sensors fused with breakthrough AI, Mantari transforms raw environmental signals into actionable intelligence so that the world’s most critical outcomes, from global trade and securing grids to defending nations, are guided not by uncertainty, but by foresight.

Our autonomous edge sensing fleets, deep learning forecast models, and integrated customer decision engines will create the most precise weather intelligence on the planet—empowering industry, strengthening climate resilience, providing vital intelligence to American warfighters, and opening billion-dollar markets that don’t exist yet.

Backed by premier venture capital and led by engineers and scientists who’ve landed rockets and pioneered novel AI forecast techniques, we move fast, own the hard problems end to end, and measure success in real world impact. If you want to build frontier tech with outsized consequences, welcome aboard.

The Head of Weather will lead Mantari’s forecasting science and model development — owning how we transform raw sensor and environmental data into world-class, real-time forecasts to solve customer problems. This role is responsible for the design, training, and validation of Mantari’s atmospheric and oceanic forecasting systems, spanning physics-based, hybrid, and AI-native models.

You’ll define the scientific strategy for Mantari’s forecasting capabilities — setting the bar for accuracy, latency, and reliability — and ensure our predictions outperform legacy systems across key markets like shipping, aviation, energy, and defense.

You’ll collaborate closely with our Growth team and engineers to align model outputs with customer decision layers and mission requirements, and drive what sensing hardware we build and deploy. Your work ensures Mantari’s forecasts are not only scientifically best-in-class, but operationally decisive.

• Own Forecasting Science:Lead the design, development, and validation of Mantari’s weather forecasting stack, including physics-based, hybrid, and AI-native models.
• Model Training and Evaluation:Guide training pipelines, data selection, tuning, and verification using proprietary and public datasets.
• Science and Research Leadership:Ensure forecasts meet meteorological standards, demonstrate skill improvement, and hold operational reliability, while tracking advancements in AI-native weather modeling and incorporating state of the art methodologies.
• Data Assimilation:Define strategies for ingesting and fusing proprietary data from in-situ distributed sensors, satellites, and third-party sources.
• Sensor Development:Drive the selection of new sensing capabilities for Mantari’s development roadmap to deliver maximum value to our customers.
• Collaboration:Partner with software engineering to deploy models at scale and connect forecast outputs to decision-support products, driving outcomes for commercial and government end-users.
• Team Building:Recruit and mentor atmospheric scientists, ML modelers, and verification specialists

• PhD or equivalent experience in Atmospheric Science,Meteorology,Climate Modeling, or related field
• 5+ years of experience in numerical weather prediction (NWP),forecast modeling, orAI/hybrid weather systems
• Deep understanding of atmospheric dynamics, model physics, and data assimilation methods
• Hands-on experience with one or more:
  • NWP systems (e.g. WRF, FV3, ICON, MPAS)
  • AI-native forecasting models (e.g. Graph Cast, Pangu-Weather, Four Cast Net , AIFS)
  • Data assimilation techniques (e.g. 3D/4D-Var, EnKF, hybrid systems)
• Experience designing, training, and verifying forecast models
• Proven ability to collaborate with engineering teams to deploy models at scale

• Experience with historical climate records and atmospheric reanalysis datasets, including…