Comand AI's mission is to build next-generation C2 software with real users in real deployments.
We are fully indexed on the success of every single user, which means our research has to work in the field, not on a benchmark.
As a Computer Vision Engineer, you own the hardest perception problems in the stack.
The job is to crack the scientific problems that prevent us from turning multi-source sensor data (satellite, drone, ISR, OSINT) into real-time operational clarity.
Your models ship into production on short cycles, used by people for whom bad output has real consequences.
In practice:Design and train architectures for object detection and segmentation on satellite and aerial imagery, and classification across complex image typesDrive our applied research agenda: track the state of the art rigorously, form hypotheses, iterate fastPush models to production readiness: optimize for inference speed and robustness under distribution shift, which will happen in field conditionsWork closely with data engineers and software engineers to move work from research to production without losing what mattersSpend time with end users and domain experts to understand what "good" actually means in their contextWho We're Looking ForWe want scientific depth combined with the instinct to ship.
Not a publication count, but evidence of both: a GitHub and Google Scholar that tell a coherent story.
A recent PhD is one path in.
A more experienced researcher with a strong collaborative instinct and a track record of shipping is equally welcome.
Must-have:PhD in AI, deep learning, or computer vision, or 5-10 years of serious applied research with a track record to back it upStrong deep learning expertise, non-negotiable.
Computer vision experience strongly preferredSolid Python, Git, and PyTorch.
JAX/Flax, scikit-learn, and Numpy are real plusesHands-on experience with Transformer and multimodal architectures applied to visual dataEU citizenship (required by our defense contracts)English f.