About the Role
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 Learning, Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs), Generative AI and Diffusion Models. Prior 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.
Responsibilities
- 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.
Requirements
- 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.
Qualifications
- 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.
Benefits
Not specified