Job Details

Overview

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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