Software Engineer - ML Infrastructure

ML Infrastructure Engineer 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. 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.

Created: 2026-03-04