Perceptive Space Systems

Perceptive Space is on a mission to make humanity resilient to space weather. Space weather refers to changing radiation levels, magnetic fields, and other conditions of the space environment. This impacts the performance and reliability of all space-borne technological systems and can endanger human health. Today, we rely solely on government agencies' forecasts to safeguard operations against space weather impact. These forecasts are limited in accuracy and do not provide the decision intelligence required to support the scale of the modern aerospace industry, resulting in operational losses in the form of premature deorbit of satellites, failed launches, GPS outages, etc. Perceptive Space is addressing this problem through its AI-powered space weather prediction system, which provides the accuracy and timely actionable insights required to support today's scale, automation, and space-based operations' autonomous future. Backed by world-class investors and a team of scientists and engineers whose collective background includes NASA, Los Alamos National Lab, MIT, and Silicon Valley, Perceptive Space is transforming how aerospace and defense industries respond to the threat of space weather.

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📍 Canada

🔍 Aerospace, AI

  • 4+ years of industry experience following a Master’s or PhD in Physics, Electrical Engineering, Applied Math, or a related field
  • 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)
  • Experience deploying ML solutions onto cloud platforms (e.g., AWS, GCP, Azure)
  • 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

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Posted 3 days ago
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📍 United States

🧭 Contract

🔍 Aerospace

  • Strong proficiency in state estimation techniques, including Kalman filtering (EKF, UKF) and batch filtering.
  • Expertise in non-linear optimization and computational mathematics.
  • Proficient in Python AND C/C++.
  • Experience working with GNSS, radar, lidar, and camera data, including sensor calibration and error modeling in real-world applications/projects.
  • Experience with modern software engineering practices: version control (Git), CI/CD, cloud-based workflows, and peer review.
  • 4+ years of industry experience with a Master’s or PhD in Aerospace Engineering, Electrical Engineering, Mechatronics, or a related field.
  • Design and implement probabilistic filtering techniques (e.g., Kalman, Extended Kalman, Particle Filters) for robust real-time state estimation and uncertainty modeling.
  • Develop sensor fusion algorithms that integrate data from multiple modalities, such as GNSS,  LiDAR, radar, and cameras, to estimate real-time state estimation and tracking.
  • Develop algorithms for outlier rejection, fault detection, and state smoothing.
  • Contribute to architecture development, concept-of-operations, and technology trade studies.
  • Collaborate with AI/ML engineers to explore machine learning-enhanced state estimation techniques

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Posted 11 days ago
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