NVIDIA

1 day ago

Senior Tools Development Engineer – Machine Learning

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 7 hours agoStatus: Live

Job Summary

Build DL/AI solutions for validating NVIDIA GPUs within the Deep Learning Focus Group. Work on intelligent gameplay automation and agentic workflows using computer vision, reinforcement/imitation learning, and LLM/VLM-based agents. Drive ML-driven QA and defect detection, and develop end-to-end, Python-based GPU validation pipelines. Establish scalable Linux/Docker/TensorRT environments and curate self-improving data pipelines.

Location: Shanghai

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Design and deploy gameplay automation and agentic workflows using computer vision, reinforcement learning, imitation learning, and LLM/VLM-based agents.
•Apply ML/DL techniques to ML-driven QA and defect detection across NVIDIA product lines, including video/audio defect detection and test framework optimization.
•Create and maintain end-to-end GPU validation solutions using Python-based automation pipelines consuming neural networks.
•Set up and manage scalable development environments using Linux, Docker, and TensorRT for training, validation, and deployment of large-scale neural networks.
•Build, clean, and augment datasets to power continuous, self-improving training pipelines.

Key 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 and problem-solving skills, plus hands-on experience with Linux and Docker.
•Solid understanding of OpenCV and DL algorithms for classification, object detection, tracking, and segmentation.
•Well-versed in QA methodologies and NVIDIA GPU technologies (e.g., RTX, DLSS), including experience with agentic gameplay systems using LLM/VLM, GenAI, RAG, and vLLM.

Experience:2+ yearsGamingDeep learningComputer visionQA automation

Education:PhD / Doctorate in AI/ML/CS

Skills:Excellent written and verbal communicationStrong initiativeSelf-motivationCommitment to high software quality standardsProblem-solving

Tech Stack:PythonPyTorchTensorFlowKerasONNXTensorRTLinuxDockerOpenCVDRLImitation LearningVision TransformersCNNGenerative AIDiffusion ModelsLLMVLMCodexClaudeCursor

Company Brief

NVIDIA

Designs and manufactures GPUs, AI accelerators, and system-on-chip products for gaming, data centers, professional visualization, and automotive markets, enabling advanced graphics, AI, and high-performance computing solutions worldwide.

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor

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NVIDIA

1 day ago

Senior Tools Development Engineer – Machine Learning

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 7 hours agoStatus: Live

Job Summary

Location: Shanghai

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Design and deploy gameplay automation and agentic workflows using computer vision, reinforcement learning, imitation learning, and LLM/VLM-based agents.
•Apply ML/DL techniques to ML-driven QA and defect detection across NVIDIA product lines, including video/audio defect detection and test framework optimization.
•Create and maintain end-to-end GPU validation solutions using Python-based automation pipelines consuming neural networks.
•Set up and manage scalable development environments using Linux, Docker, and TensorRT for training, validation, and deployment of large-scale neural networks.
•Build, clean, and augment datasets to power continuous, self-improving training pipelines.

Key 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 and problem-solving skills, plus hands-on experience with Linux and Docker.
•Solid understanding of OpenCV and DL algorithms for classification, object detection, tracking, and segmentation.
•Well-versed in QA methodologies and NVIDIA GPU technologies (e.g., RTX, DLSS), including experience with agentic gameplay systems using LLM/VLM, GenAI, RAG, and vLLM.

Experience:2+ yearsGamingDeep learningComputer visionQA automation

Education:PhD / Doctorate in AI/ML/CS

Skills:Excellent written and verbal communicationStrong initiativeSelf-motivationCommitment to high software quality standardsProblem-solving

Tech Stack:PythonPyTorchTensorFlowKerasONNXTensorRTLinuxDockerOpenCVDRLImitation LearningVision TransformersCNNGenerative AIDiffusion ModelsLLMVLMCodexClaudeCursor

Company Brief

NVIDIA

Industry: Electronics Manufacturing

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Santa Clara, United States

Founded: 1993

Glassdoor: 4.3

Website LinkedIn Glassdoor