Description:

Perceptive Space Systems is developing a decision intelligence platform to assist satellite and launch operators in managing risks from space weather and the space environment.
The company operates at the intersection of aerospace, AI, and real-time systems, utilizing advanced modeling, sensor fusion, and autonomy to enhance operational resilience in orbit.
The Machine Learning Scientist will create foundational technology for satellites, launch vehicles, and human missions to function safely and efficiently in the challenging space environment.
The role involves working in a small, high-velocity team to tackle real-world challenges with immediate mission impact.
Responsibilities include building and evaluating machine learning models for time series forecasting and spatio-temporal dynamics, designing experiments to assess model generalization, and integrating domain knowledge to enhance model performance.
The position requires collaboration with aerospace engineers, software engineers, and domain experts to deploy ML systems in production and staying updated on developments in ML for dynamic systems.

Requirements:

Candidates must have 4+ years of industry experience following a Master’s or PhD in Physics, Electrical Engineering, Applied Math, or a related field.
Experience in fast-paced, high-ownership ML roles within a startup or demanding environment is required.
Proficiency in Python and experience with deep learning frameworks such as PyTorch or TensorFlow is necessary.
Familiarity with tools and frameworks like MLflow, Ray, Dask, and Numba is expected.
A strong background in modeling temporal or sequential data, such as time series forecasting and signal processing, is essential.
Candidates should be comfortable working with multidimensional datasets and integrating domain context into modeling.
A solid foundation in software engineering practices, including coding standards, code reviews, source control (e.g., Git), build processes, and testing, is required.
Experience deploying ML solutions on cloud platforms like AWS, GCP, or Azure is necessary.
A proven track record of contributing to the successful delivery of production-ready ML models is essential.
Candidates must be able to clearly explain model behavior, assumptions, and limitations to both technical and non-technical stakeholders.
Excellent communication and collaboration skills are required to work effectively across disciplines.
Bonus qualifications include experience in early-stage or cross-disciplinary R&D teams, scientific modeling, and familiarity with uncertainty quantification techniques.

The position offers generous stock option compensation.
Employees receive top-tier health and benefits coverage.
The team operates fully remote, providing flexibility in work location.
There are opportunities to lead technical efforts as the team scales, allowing for professional growth and leadership development.