Machine Learning Engineer

Overview Lightningware is looking for a Machine Learning Engineer focused on AI Safety and Moderation to help build the next generation of intelligent safety systems for our live video platform, Thundr and future products. Thundr operates a real-time video chat service where trust, safety, and moderation are critical to the user experience. We already have moderation systems in production, and we are looking for an engineer who can help us significantly expand their capabilities. Improving detection accuracy, reducing response times, scaling our infrastructure, and researching new ways AI can make real-time online interactions safer. This role will work across computer vision, multimodal AI, machine learning infrastructure, real-time inference, and applied AI research . You will help develop systems capable of analyzing live video, images, audio, text, and behavioral signals to identify policy-violating or unsafe activity at scale. You will also play an important role in an upcoming Lightningware project where we are building new moderation technology from the ground up. This is a highly applied engineering role. We are looking for someone who enjoys taking emerging AI capabilities and turning them into reliable production systems operating at significant scale.

Responsibilities Design and build machine learning systems for real-time moderation of live video interactions . Develop computer vision and multimodal models capable of detecting unsafe, inappropriate, abusive, or policy-violating content. Research and evaluate emerging AI models and techniques that could improve Thundr’s moderation and safety capabilities. Improve existing moderation systems by optimizing precision, recall, latency, throughput, and cost . Develop systems for model evaluation, benchmarking, dataset creation, labeling, and continuous improvement. Build scalable inference infrastructure capable of processing large volumes of real-time user activity. Design moderation architectures that intelligently combine automated detection, confidence thresholds, escalation systems, and human review. Investigate techniques for detecting adversarial behavior and users attempting to circumvent automated moderation systems. Deploy and monitor machine learning models in production environments. Develop tools that allow our team to understand model decisions, review incidents, and improve moderation policies. Collaborate closely with engineering and leadership teams on new products and safety initiatives. Maintain strong documentation around model architecture, evaluation methodology, datasets, deployment systems, and moderation experiments.

Qualifications 5+ years of professional software engineering, machine learning, or applied AI experience , including substantial experience building and operating machine-learning systems in production. At least 2+ years working directly with production computer-vision or image/video ML systems . Strong Python engineering skills , including experience building maintainable production services. Not only research code, notebooks, or prototypes. Strong experience with PyTorch and modern deep-learning workflows. Strong practical knowledge of computer vision , including experience with areas such as image classification, object detection, OCR, video analysis, or vision transformers. Hands-on experience deploying and optimizing inference workloads on NVIDIA GPUs / CUDA . Experience designing and evaluating ML systems using metrics such as precision, recall, false-positive rate, false-negative rate, and confidence thresholds . Experience creating or working with labeled evaluation datasets and using them to measure model performance and regression. Experience building production data and inference pipelines and integrating ML systems with APIs, queues, or other distributed services. Experience with Docker, Linux, and cloud infrastructure . Strong software-engineering fundamentals, including reliability, observability, testing, failure handling, and performance optimization. Ability to independently investigate an ML problem, evaluate competing approaches, build prototypes, measure results, and deploy a production solution. Bachelor’s degree in Computer Science, Machine Learning, Engineering, or a related technical field, or equivalent practical experience . Preferred