Senior AI/ML Scientist, Planetary Science

Relativity Space•Long Beach, CA

1d•Hybrid

About The Position

Relativity Space is seeking an AI/ML Scientist to develop and deploy machine learning systems for their 2028 Mars orbital mission. This role involves working at the intersection of frontier AI methods and planetary science, addressing challenges with disparate datasets, sparse observations, heterogeneous instrument modalities, and dynamic planetary systems. The scientist will build AI models to run on spacecraft in Mars orbit, collaborating with Relativity's Interplanetary Sciences Program and Polymathic AI. Key responsibilities include enhancing Mars atmospheric modeling and weather forecasting by applying ML techniques to combine Earth-derived atmospheric datasets and Martian atmospheric physics, optimizing models for spacecraft operation. Another challenge is multi-modal data fusion, where the scientist will develop methods to integrate various datasets (2D images, 3D models, geologic maps, radar soundings) to reconstruct coherent 3D representations for autonomous in situ science. This includes building systems for real-time observation monitoring, analysis, and detection of scientifically significant events, as well as developing an AI decision-making layer for autonomous spacecraft re-tasking. This is a high-ownership, applied research role on a lean team, requiring end-to-end problem ownership, system building, evaluation, and clear communication. The goal is to amplify science discovery for the Mars mission by combining core ML principles with deep learning tools.

Requirements

PhD in machine learning, computer science, physics, or a related technical field.
3+ years of relevant industry experience.
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.

Responsibilities

Develop and deploy machine learning systems to unlock new science from the 2028 Mars orbital mission.
Build AI models to run on the spacecraft in Mars orbit.
Develop and apply Machine Learning techniques to combine Earth-derived atmospheric datasets and known Martian atmospheric physics to create a weather forecasting model for real-time input on the spacecraft.
Optimize the weather forecasting model to run on the spacecraft at Mars.
Develop and build methods that reconstruct coherent 3D representations by integrating complementary datasets (2D surface images, 3D surface models, geologic mapping, radar depth soundings).
Apply these approaches to autonomous in situ science.
Build systems that monitor observations, analyze them in real-time on the spacecraft, and detect scientifically significant events.
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.
Drive problem framing, build and evaluate systems end-to-end, and communicate results clearly to scientists and engineers.
Combine core principles of machine learning with practical tools of deep learning to amplify science discovery.