Senior Tools Development Engineer – Machine Learning

This role within Deep Learning Focus Group is strongly technical, responsible for building DL/AI based solutions for validation of NVIDIA GPUs. For e.g. GPU Render Output Analysis (Video, Images, Audio) and complex problems like Intelligent Game Play Automation. This person would need to analyze/understand the challenges from stakeholders of various groups, design & implement DL solutions to resolve them. We are specifically looking for expertise in the following key areas:Game play automation using deep learning (DL) and artificial intelligence (AI) techniques, with a focus on research and application in the gaming industry.The candidate should be proficient in the following DL techniques:Deep Reinforcement Learning (DRL) and Imitation LearningVision Transformers (ViTs) and Convolutional Neural Networks (CNNs)Generative AI and Diffusion ModelsPrior experience or research in AI-driven bots, In-Game Movement Automation, and Player Behavior Prediction are highly desirable.Knowledge in using AI development tools for test plans creation, test cases development and test cases automation.What you'll be doing:Build Intelligent Gameplay Automation & Agentic Workflows: Design and deploy advanced gameplay agents using computer vision, reinforcement learning, imitation learning, and LLM/VLM-based agents, leveraging state-of-the-art tools like Codex and Claude.Drive ML-Driven QA & Defect Detection: Apply ML/DL techniques to solve complex QA challenges across NVIDIA product lines, implementing DL-based solutions for video/audio defect detection and optimizing automated test frameworks to boost productivity.Develop End-to-End GPU Validation Solutions: Create and maintain robust, Python-based automation pipelines that consume neural networks to rigorously validate NVIDIA GPUs.Establish Scalable Infrastructure & Deployment: Set up and manage scalable development environments using Linux, Docker, and TensorRT to train, validate, and deploy large-scale neural networks.Curate Self-Improving Data Pipelines: Build, clean, and augment high-quality datasets to feed and enable continuous, self-improving training pipelines.What we need to see:Master’s or PhD in AI/ML/CS (or equivalent) with at least 2 years of hands‑on ML engineering experience.Extensive knowledge of PyTorch, TensorFlow/Keras, ONNX, and TensorRT.Advanced Python proficiency with strong OOP, design, and problem-solving skills for large-scale applications, combined with familiarity and hands-on experience in Linux and Docker.Solid understanding of OpenCV and state-of-the-art DL algorithms for image classification, object detection, tracking, and segmentation.Well-versed in QA methodologies with a deep understanding of NVIDIA GPU technologies (e.g., RTX, DLSS).Hands-on experience building agentic gameplay systems using LLM/VLM-based agents, GenAI, RAG, vLLM, and solving complex problems with AIGC; proficient with AI development tools (Codex, Cursor, MCP, CodeRabbit) for test automation and workflow acceleration.Excellent written and verbal communication, strong initiative, self-motivation, and a commitment to high software quality standards.Ways to stand out from the crowd:Knowledge of Transformer based LLM and AIGC, Imitation Learning, Model free/based RL, Hierarchical RL, Inverse RL, Meta-learning, Life-long learning.Hands-on experience in solving complex problems using Deep learning Algorithms would be a plus.Experience and medals in data science or computer vision competitions (e.g., Kaggle, CVPR workshop) will be a plus.Demonstrated ability to rapidly understand game dynamics and decompose game scenarios into solvable DL problems that can be implemented end‑to‑end.