Standardized satellite platform for fast and reliable low Earth orbit missions.
We’re Making Space Simple: Loft is a space infrastructure company that provides simple, fast, and reliable ways to deploy missions to low Earth orbit.
We have developed a standardized satellite platform that can be configured to fly any customer mission. With our own hardware and software abstraction layers on top of a commodity satellite bus, our platform is truly turnkey and plug-and-play.
Loft offers our customers unbeatable speed-to-orbit by removing the non-recurring engineering that is the root cause of schedule unpredictability and cost overruns. We bypass typical aerospace lead times by maintaining an inventory of fully assembled satellite platforms at our Integration and Test facility in Golden, CO.
Loft handles all elements of the process of deploying and operating space infrastructure on the customer’s behalf, so that customers can focus their time and resources entirely on their mission.
Wanna join the adventure?
Loft's AI for Space group is building the stack that runs real machine learning in orbit; turning raw sensor data into decisions on the satellite itself, so insight reaches the customer in minutes instead of hours.
The AI Engineering team is where models meet the spacecraft. We own the full path from a partner's trained model to something that actually runs — fast — on flight hardware: compiling ONNX models into optimized engines for each accelerator, the Space Inference Engine and its execution providers, the SDK that packages models for flight, and the perf tooling that proves a model fits its power, thermal, and latency budget before it ever leaves the ground. As we open the platform to a growing roster of defense and Earth-observation partners, the team's scope and the range of silicon we target are expanding fast.
As our ML Runtime Engineer, you'll own model compilation and the performance tooling behind it. We're looking for someone scrappy — the kind of engineer who takes an unfamiliar model and an unforgiving power budget, digs into the graph, and gets it running on real hardware without waiting for the path to be handed to them. You'll move between model compilation, runtime internals, quantization, and hardware-in-the-loop CI, sometimes in the same afternoon.
In short
Founding year
2017
Employees
303
Gender
35%
65%