Overview Direct message the job poster from recruyt Our client 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 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. Seniority level Mid-Senior level Employment type Full-time Job function Industries Security and Investigations, Robotics Engineering, and Defense and Space Manufacturing

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