AI/ML Scientist Planetary Science

About the Team: 

Relativity Space is on a mission to better connect humanity to space and what’s beyond our planet with its launch vehicle the Terran R. Its Interplanetary Sciences Program was established to expand access to scientific exploration across our Solar System with the mission to push the boundaries of how planetary science is done and make planetary research faster more affordable and more capable than ever before. We are rethinking how science missions are designed built and operated and how the collected data is analyzed and used. We are transforming space science from an occasional event into a continuous process of discovery that accelerates knowledge broadens participation and inspires the next generation of explorers.

About the Role:

We are seeking an AI/ML Scientist to develop and deploy machine learning systems that unlock new science from our 2028 Mars orbital mission. This is a rare opportunity to work at the intersection of frontier AI methods and planetary science - building new approaches for a data environment with disparate datasets and often sparse observations heterogeneous instrument modalities and a dynamic planetary system we are only beginning to understand. The problems will be diverse and the solutions open-ended. You will be building AI models to run on the spacecraft in Mars orbit. This position is jointly advised by Relativity's Interplanetary Sciences Program and Polymathic AI a research collaboration initiative pioneering foundation models for scientific data across physical disciplines.

One topic is enhancing Mars atmospheric modeling and doing weather forecasting. The historical record of Mars weather is fragmentary. You will develop and apply Machine Learning techniques to combine Earth-derived atmospheric datasets and known Martian atmospheric physics to create a weather forecasting model to be run on the spacecraft at Mars with real-time collected data as the input. This development includes optimizing the weather forecasting model to run on the spacecraft at Mars.

Another challenge is multi-modal data fusion. You will develop and build methods that reconstruct coherent 3D representations by integrating complementary datasets of 2D surface images 3D surface models geologic mapping of units and radar depth soundings each having different geometry resolution temporal cadence and past and new data.

These approaches will then be applied to autonomous in situ science. You will build systems that monitor observations analyze them in real-time on the spacecraft and detect scientifically significant events based on known phenomenology of Mars as well as novelty detection. Critically you will develop the AI decision-making layer that closes the loop autonomously re-tasking the spacecraft to acquire follow-up observations from onboard inference on flight hardware. This capability is central to the mission architecture and represents one of the most ambitious applications of autonomous science in any planetary mission to date.

This is a high-ownership applied research role on a lean team. You will drive your own problem framing build and evaluate systems end-to-end and communicate results clearly to scientists and engineers alike. Fulfilling this objective requires creativity to combine core-principles of machine learning to the practical tools of deep learning with a laser focused goal to amplifying the science discovery of the Mars mission.

The selected candidate will work in close collaboration with Interplanetary Sciences Team at Relativity headed by Dr. Margarita Marinova and Polymathic AI headed by Prof. Shirley Ho at Simons Foundation and New York University. The collaboration requires some travel to New York.

The selected candidates will join a vibrant interdisciplinary team based in Long Beach CA and New York City spanning NYU and the Flatiron Institute composed of rocket scientists machine learning researchers engineers and other domain scientists. This collaborative environment at Relativity and Polymathic AI offers a unique opportunity to work on cutting edge AI models and advance AI for planetary discovery.

About You

• PhD in machine learning computer science physics or a related technical field
• Demonstrated experience with transfer learning domain adaptation or model fine-tuning particularly in low-data or out-of-distribution settings
• Experience with applying machine learning in physical datasets
• Working knowledge of multi-modal data fusion
• Ability to own problems end-to-end: from dataset understanding through model development evaluation and deployment
• Excited to collaborate with a diverse group of scientists and engineers and further planetary science

This position may require occasional travel to partner institutions test facilities and mission integration sites (<10% time). During critical mission phases including launch commissioning and early operations this position will require shift work and on-call availability. 

What We Do

Relativity Space is on a mission to better connect humanity to space and the universe beyond our planet. Our medium-to-heavy lift reusable rocket Terran R will deliver customer payloads to orbit meeting the growing demand for launch capacity. But that’s just the start. Achieving commercial success with Terran R will unlock new opportunities to advance science exploration and innovation pioneering progress that reaches beyond the known.

Why Work With Us

Joining Relativity means becoming part of something where autonomy ownership and impact exist at every level. Here you're not just executing tasks; you're solving problems that haven’t been solved before helping develop a rocket a factory and a business from the ground up.

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