Company Background Specter is creating a software-defined "control plane" for the physical world. We are starting with protecting American businesses by granting them ubiquitous perception over their 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. Role + Responsibilities Specter is hiring an ML infrastructure engineer to build and scale the machine learning systems that power real-time perception and inference across our edge-cloud platform. This role owns the training, deployment, and optimization of computer vision and sensor fusion models that enable autonomous monitoring and decision-making for our customers' physical assets. Key responsibilities include: • Designing and implementing scalable ML training pipelines for computer vision models (object detection, tracking, classification, segmentation). • Building efficient model serving infrastructure for real-time inference on edge devices with constrained compute and power budgets. • Optimizing models for deployment on embedded hardware (quantization, pruning, TensorRT, ONNX, CoreML). • Developing continuous training and evaluation systems to improve model performance from production data feedback loops. • Creating data pipelines for ingesting, labeling, versioning, and managing massive multi-modal sensor datasets (video, radar, lidar, thermal). • Implementing model monitoring, A/B testing frameworks, and performance analytics for deployed perception systems. • Collaborating with perception researchers to transition models from research to production at scale across thousands of edge nodes. • Building tools and infrastructure for distributed training, hyperparameter optimization, and experiment tracking. Preferred Qualifications • Strong experience with ML frameworks (PyTorch, TensorFlow) and model optimization tools (TensorRT, ONNX Runtime, OpenVINO). • Deep understanding of computer vision architectures and their deployment tradeoffs (YOLO, transformers, CNNs, real-time detection/tracking). • Hands-on experience deploying models on edge devices (NVIDIA Jetson, ARM processors, or similar embedded platforms). • Expertise building MLOps infrastructure — experiment tracking (Weights & Biases, MLflow), feature stores, model registries, CI/CD for ML. • Experience with distributed training frameworks (PyTorch DDP, DeepSpeed, Ray) and GPU cluster management. • Strong software engineering skills in Python and systems languages (C++, Rust) for performance-critical inference code. • Familiarity with video processing, sensor fusion, or multi-modal perception systems is a plus. • Prior experience in robotics, autonomous systems, or real-time ML applications is highly valued.