Sr. Machine Learning Scientist

Perceptive Space Systems is building a decision intelligence platform to help satellite and launch operators navigate the growing risks posed by space weather and the space environment. We work at the intersection of aerospace, AI, and real-time systems, combining cutting-edge modeling, sensor fusion, and autonomy to improve operational resilience in orbit. Read more here.Join us at the frontier of space technology and AIYou will build the foundational technology required for satellites, launch vehicles, and human missions to operate safely and efficiently in the harsh space environmentAs part of our small, high-velocity team, you'll work at the intersection of aerospace, autonomy, and applied AI, solving real-world challenges with immediate mission impact.This role is ideal for entrepreneurial engineers who want to build from first principles, move fast, and own core systems end-to-end and who take initiative, thrive in ambiguity, and be part of a demanding startup environment. What You'll DoBuild and evaluate machine learning models for time series forecasting and spatio-temporal dynamicsDesign experiments to assess model generalization, uncertainty, and relevance to physical systemsIntegrate domain knowledge, external signals, or prior constraints to improve model performanceOptimize model performance through feature engineering, architecture tuning, and validation strategiesCollaborate with aerospace engineers, software engineers, and domain experts to deploy ML systems in productionStay up to date with developments in ML for dynamic systems, forecasting, and scientific MLRequirements4+ years of industry experience following a Master's or PhD in Physics, Aerospace, Electrical Engineering, Applied Math, or a related field Experience in fast-paced, high-ownership ML roles within a startup or a fast-moving, demanding startup-like environmentProficient in Python and experienced with deep learning frameworks such as PyTorch or TensorFlowExperienced with tools and frameworks like MLflow, Ray, Dask, and NumbaStrong background in modeling temporal or sequential data (e.g., time series forecasting, state-space models, signal processing)Comfortable working with multidimensional datasets and integrating domain context into modelingStrong general foundations in software engineering, including coding standards, code reviews, source control (e.g., Git), build processes, and testingExperience deploying ML solutions onto cloud platforms (e.g., AWS, GCP, Azure)Track record of contributing to the successful delivery of production-ready ML modelsAble to explain model behavior, assumptions, and limitations clearly to both technical and non-technical stakeholdersExcellent communication and collaboration skills; able to work effectively across disciplinesBonus If You HaveExperience working in early-stage start ups or cross-disciplinary R&D teamsExperience working on scientific modeling, simulation data, or systems governed by physics or control principlesFamiliarity with techniques for uncertainty quantification and physics-informed MLA track record of publications or contributions to open-source ML librariesProficient in C/C++ and JavaAdditional RequirementsThe role is fully remote, BUT you are expected to be available during Eastern Time working hours. BenefitsOpportunity to work at the frontier of AI and aerospace, building first-of-its-kind productsCompetitive stock option compensationTop-tier health and benefits coverageFully remote teamOpportunities to lead technical efforts as the team scales