Location: Remote / Honolulu, HI

Overview:

Design and train predictive models that optimize real-time satellite routing and link selection. You will work with orbital telemetry, RF datasets, and reinforcement learning frameworks to minimize latency and packet loss across simulated and live constellations.

Responsibilities:

• Develop and deploy ML algorithms for dynamic path optimization.

• Integrate models with satellite telemetry APIs and ground-station data.

• Contribute to Astralis’ core optimization engine (AegisNet™).

  Qualifications:

• Strong background in Python, PyTorch, or TensorFlow.

• Experience with time-series or reinforcement learning.

• Familiarity with orbital mechanics or network optimization preferred.

Location: Honolulu, HI / Hybrid

Overview:

Bridge hardware, software, and orbital models to ensure seamless communication performance across Astralis’ routing and simulation systems.

Responsibilities:

• Model end-to-end communication links between orbit and ground.

• Validate FSPL, SNR, and throughput in simulation and live tests.

• Collaborate with data science and DevOps teams on integration.

  Qualifications:

• B.S./M.S. in Systems, Aerospace, or Electrical Engineering.

• Knowledge of satellite communication standards (ITU, CCSDS).

• Familiarity with Polars, MATLAB, or similar analytical tools.

Location: Remote

Overview:

Build the user interface for Astralis’ network analytics and simulation dashboard. You’ll translate complex telemetry into intuitive visual systems for operators and partners.

Responsibilities:

• Develop React/TypeScript dashboards for performance visualization.

• Collaborate with backend and ML teams to display live metrics.

• Implement map and orbital 3D visualizations (CesiumJS or Three.js).

  Qualifications:

• Proven experience in modern front-end frameworks (React, Next.js).

• Strong design sense and data visualization experience.

• Interest in aerospace or complex systems visualization.