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About the role
Specter is hiring an applied AI software engineer to own the path from model to product. You'll take detection, tracking, and vision-language models from research prototypes to reliable production systems running across a distributed fleet of edge devices and cloud inference services, then close the loop by measuring how those models actually perform in the field and feeding that signal back into the next iteration.
What they're looking for
- Experience taking ML models from research code to production systems that real users depend on
- Strong Python skills, with working knowledge of PyTorch and modern inference runtimes (ONNX, TensorRT, or similar)
- Track record building evaluation pipelines and defining metrics for model performance in deployment, including handling distribution shift and unlabeled production data
- Experience with data pipelines and annotation workflows for computer vision or other perception domains
- Comfort with infrastructure: Docker, Kubernetes, cloud GPU services, and CI/CD for model artifacts
- Familiarity with computer vision (object detection, multi-object tracking, re-identification) or vision-language models
More about this role
Specter is creating a software-defined "control plane" for the physical world. We are starting with providing ubiquitous perception over physical assets.
To do so, we are creating a connected hardware-software ecosystem on top of multi-modal wireless mesh sensing technology. This allows us to drive down the cost and time of deploying sensors by 10x. Our platform will ultimately become the perception engine for a company's physical footprint, enabling real-time perimeter visibility and autonomous operations management.
Our co-founders Xerxes and Philip are passionate about empowering our partners in the fast approaching world of physical AI and robotics. We are a small, fast growing team who hail from Anduril, Tesla, Uber, and the U.S. Special Forces.
Specter is hiring an applied AI software engineer to own the path from model to product. You'll take detection, tracking, and vision-language models from research prototypes to reliable production systems running across a distributed fleet of edge devices and cloud inference services, then close the loop by measuring how those models actually perform in the field and feeding that signal back into the next iteration. This role sits...
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