Offer summary

Qualifications:

Strong proficiency in state estimation techniques, including Kalman filtering and batch filtering., Expertise in non-linear optimization and computational mathematics., Proficient in Python and C/C++ with experience in sensor data integration., 4+ years of industry experience with a Master’s or PhD in a relevant engineering field..

Key responsibilities:

Design and implement probabilistic filtering techniques for real-time state estimation.

Develop sensor fusion algorithms to integrate data from multiple modalities.

Contribute to architecture development and technology trade studies.

Collaborate with AI/ML engineers to enhance state estimation techniques.

Job description

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 AI

You will build the foundational technology required for satellites, launch vehicles, and human missions to operate safely and efficiently in the harsh space environment

We're building advanced navigation and state estimation systems for next-generation space vehicles and sensing platforms. As 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 care deeply about building things that work in the real world.

What You’ll Do

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

Requirements

Minimum Qualifications

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.
Excellent communication and collaboration skills; able to work effectively across disciplines.
Experience working in a fast-paced, process-oriented, high-ownership, and demanding startup environment.

Preferred Qualifications

Experience with uncertainty quantification and Monte Carlo methods.
Exposure to machine learning frameworks (e.g., PyTorch, TensorFlow).
Experience with spacecraft guidance, navigation, and control (GNC) or orbit determination.
Experience with Rust or Go.

NOTE: Applicants must be U.S. Citizens or Permanent Residents. Visa sponsorship is not available for this position.

Benefits