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# Machine Learning Engineer

- **Company:** LIGHTNINGWARE LLC
- **Location:** Scottsdale, AZ, United States
- **Experience:** Experienced
- **Salary:** $130,000.0 - $180,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Computer Vision, Microsoft Azure, Cloud Computing, Data Files, Distributed Systems, FFmpeg, Fraud Prevention and Detection, Monitoring of Systems, Python (Programming Language), Machine Learning, Language Modeling, Natural Language Processing, OpenCV, Tensorflow, Software Engineering, WebRTC, Graphics Processing Unit (GPU), Pytorch, Deep Learning, Model Validation, Build Management, Information Technology, ONNX (Open Neural Network Exchange) Format, Machine Learning Operations, TensorRT, Data Pipelines
- **Published:** September 13, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=1ad9fccf8d836fe7

## About the Role

* Strong experience building and deploying machine learning or AI systems in production.
 * Bachelor's degree in Computer Science, Machine Learning, Artificial Intelligence, Engineering, or a related technical field, or equivalent practical experience. Master's degree is a plus, but not required.
 * 3+ years of professional experience in machine learning engineering, applied AI, computer vision, or a related field, with experience deploying ML systems into production.
 * Strong Python programming skills and experience with modern ML frameworks such as PyTorch, TensorFlow, or similar technologies.
 * Experience with computer vision, multimodal AI, NLP, classification systems, or content moderation.
 * Understanding of modern deep-learning architectures including transformers and vision models.
 * Experience designing model evaluation frameworks and working with metrics such as precision, recall, F1, false-positive rates, and false-negative rates.
 * Experience building data pipelines for training, evaluation, and inference.
 * Familiarity with cloud infrastructure such as AWS, GCP, or Azure.
 * Experience deploying scalable model inference systems through APIs, containers, GPUs, or distributed infrastructure.
 * Strong understanding of software engineering principles and the ability to build reliable production systems.
 * Ability to independently research new AI techniques, evaluate their usefulness, and turn promising approaches into working prototypes., * Experience building trust & safety, content moderation, fraud detection, abuse prevention, or platform integrity systems.
 * Experience processing live or near-real-time video streams.
 * Experience with technologies such as OpenCV, FFmpeg, WebRTC, or video-processing pipelines.
 * Experience working with multimodal foundation models or vision-language models.
 * Experience with GPU optimization and high-throughput inference.
 * Experience designing annotation or labeling pipelines for sensitive datasets.
 * Familiarity with model serving technologies such as Triton, ONNX Runtime, TensorRT, Ray, or similar systems.
 * Experience with MLOps, model monitoring, experiment tracking, and automated retraining pipelines.
 * Experience researching emerging AI techniques and rapidly prototyping new approaches.

## Description

* 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.