Senior AI/ML Scientist, Planetary Science

Relativity Space•Long Beach, CA

1d•$154,000 - $211,000•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 a dynamic planetary system. The scientist will build AI models to run on spacecraft in Mars orbit, collaborating with Relativity's Interplanetary Sciences Program and Polymathic AI. Key areas of focus include enhancing Mars atmospheric modeling and weather forecasting using machine learning to combine Earth-derived datasets and Martian physics, and developing multi-modal data fusion methods to reconstruct 3D representations from various datasets. The role also involves building systems for autonomous in situ science, including real-time analysis of observations, detection of significant events, and autonomous spacecraft re-tasking based on onboard inference. This is a high-ownership, applied research position requiring creativity in applying machine learning principles to amplify scientific discovery. The selected candidate will work closely with teams at Relativity and Polymathic AI, with potential travel to New York. The team is based in Long Beach, CA and New York City, comprising rocket scientists, machine learning researchers, engineers, and domain scientists.

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 for a 2028 Mars orbital mission.
Enhance Mars atmospheric modeling and develop weather forecasting models using machine learning.
Combine Earth-derived atmospheric datasets and known Martian atmospheric physics for weather forecasting.
Optimize weather forecasting models to run on spacecraft in Mars orbit.
Develop and build methods for multi-modal data fusion to integrate complementary datasets (2D images, 3D models, geologic maps, radar soundings).
Reconstruct coherent 3D representations by integrating diverse datasets.
Build systems for autonomous in situ science, monitoring observations and analyzing them in real-time.
Detect scientifically significant events based on known Martian phenomenology and novelty detection.
Develop AI decision-making layers to autonomously re-task the spacecraft for follow-up observations based on onboard inference.
Drive problem framing, build and evaluate systems end-to-end, and communicate results to scientists and engineers.
Apply core principles of machine learning and deep learning to amplify science discovery.