About the Team
The 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 the Interplanetary Sciences Team at Relativity, 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 the Flatiron Institute/Polymathic AI (about 10% time).