SOFTWARE ENGINEER (CIVIL AVIATION)
Company: Air Space Intelligence
Location: Boston, MA
Type: Full-time
Posted: 2026-04-23
About this role
LOCATION
BOSTON, US
TYPE
FULL TIME
About Air Space Intelligence
ASI's mission-critical technology powers decision-making across aviation, defense, energy, and other critical infrastructure domains. Backed by top-tier investors including Andreessen Horowitz, Spark Capital, and Renegade Partners, ASI delivers operational decision superiority—compressing days of analysis into seconds of action. ASI is leading the way and pushing the boundaries of what’s possible.
About the Civil Aviation Team
We are modernizing America’s air traffic control system on an accelerated timeline, tackling one of the world’s most complex real-time optimization challenges: safely coordinating tens of thousands of daily flights in a dynamic, safety-critical environment. Our team is building next-generation trajectory prediction and decision-support systems while preparing the airspace for emerging technologies like commercial space operations and advanced air mobility. This mission-critical work demands exceptional precision, resilience, and reliability to support the safety and efficiency of the national airspace.
What You Will Do:
As a Software Engineer for the Civil Aviation team, you will design and build a backend system that will operate at the center of U.S. air travel for the next decade and beyond. This is a platform that needs to be right the first time and maintainable by future engineers who had no hand in building it.
The core technical challenge is data fusion at operational scale: ingesting disparate, noisy, and potentially conflicting data streams, from radar, flight plans, weather systems, operator inputs, and more, and resolving them into a single, authoritative view of the world. From there, the system projects that state forward in time, enabling optimization across a complex set of competing conditions and constraints. The person in this role needs to have strong intuitions about how to model uncertainty, how to reason about data provenance and confidence, and ho...