Software Engineer - Applied AI

The role

You will take detection, tracking, and vision-language models from research prototypes to production systems. You will own evaluation infrastructure, data pipelines, and deployment mechanics for edge devices and cloud inference services.

What you will do

• Productionize models from the research team via containerization, inference optimization, and deployment.
• Build and own offline and online evaluation systems.
• Design data feedback loops to surface failure cases and create training sets.
• Define and instrument metrics for perception quality in deployment.
• Ship product features from inference service to API.
• Run experiments and staged rollouts across the fleet.
• Manage inference cost and performance tradeoffs across hardware targets.
• Collaborate with research and platform to make model iteration a routine deployment.

Required qualifications

• Experience taking ML models from research code to production systems.
• Strong Python skills.
• 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.
• 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.
• Experience with streaming or event-driven data systems (Kafka, Redpanda, or equivalent).
• Product instinct.
• Interest in physical AI, IoT, or large-scale distributed sensing systems.

About the company

Specter is creating a software-defined control plane for the physical world using multi-modal wireless mesh sensing technology. The team hails from Anduril, Tesla, Uber, and the U.S. Special Forces.