Machine Learning Scientist

November 26

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Perceptive Space

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Aerospace • Artificial Intelligence • Science

Perceptive Space is an innovative company that harnesses the power of AI to provide advanced predictions and decision intelligence for managing space weather risks. By revolutionizing traditional space weather models, Perceptive Space offers accurate, real-time updates tailored to specific orbits and missions, enhancing spacecraft operations and minimizing downtime. Their solutions are designed for the new space economy, providing actionable insights and seamless integrations for spacecraft operators and launch providers.

2 - 10 employees

🚀 Aerospace

🤖 Artificial Intelligence

🔬 Science

📋 Description

• - Build and evaluate machine learning models for time series forecasting and spatio-temporal dynamics • - Design experiments to assess model generalization, uncertainty, and relevance to physical systems • - Integrate domain knowledge, external signals, or prior constraints to improve model performance • - Optimize model performance through feature engineering, architecture tuning, and validation strategies • - Collaborate with aerospace engineers, software engineers, and domain experts to deploy ML systems in production • - Stay up to date with developments in ML for dynamic systems, forecasting, and scientific ML

🎯 Requirements

• - 2+ 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 environment. • - Proficient in Python and experienced with deep learning frameworks such as PyTorch or TensorFlow • - Experienced with tools and frameworks like MLflow, Ray, Dask, and Numba • - Strong 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 modeling • - Strong general foundations in software engineering, including coding standards, code reviews, source control (e.g., Git), build processes, and testing • - Experience deploying ML solutions onto cloud platforms (e.g., AWS, GCP, Azure) • - Track record of contributing to the successful delivery of production-ready ML models • - Able to explain model behavior, assumptions, and limitations clearly to both technical and non-technical stakeholders • - Excellent communication and collaboration skills; able to work effectively across disciplines • **Bonus If You Have** • - Experience working in early-stage start ups or cross-disciplinary R&D teams • - Experience working on scientific modeling, simulation data, or systems governed by physics or control principles • - Familiarity with techniques for uncertainty quantification and physics-informed ML • - A track record of publications or contributions to open-source ML libraries

🏖️ Benefits

• - Opportunity to work at the frontier of AI and aerospace, building first-of-its-kind products. • - Competitive stock option compensation • - Top-tier health and benefits coverage • - Fully remote team • - Opportunities to lead technical efforts as the team scales.