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Pytorch Onnx Jobs (NOW HIRING)

Advanced Micro Devices

Practical experience with AI/ML inference workflows and frameworks such as PyTorch, ONNX, ONNX Runtime, Hugging Face, or equivalent technologies. Examples of AI applications built on Windows laptops ...

Qualcomm

AI Model Optimization Architect

San Diego, CA · On-site

... PyTorch, ONNX, and torch.compile, including model rewrites and graph-level transformations. • Design and implement fusion kernels using DSL based approaches (e.g., Triton), enabling fused ...

Intel

AI Performance Library Architect

Folsom, CA · On-site

$170K - $315K/yr

... Pytorch, ONNX Runtime, and more. In this role, you will be responsible for design, development, and maintenance of new functionality in oneDNN to enable performance critical portions of AI workloads.

Intel

AI Performance Library Architect

Hillsboro, OR · On-site

$170K - $315K/yr

... Pytorch, ONNX Runtime, and more. In this role, you will be responsible for design, development, and maintenance of new functionality in oneDNN to enable performance critical portions of AI workloads.

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Pytorch Onnx information

What is PyTorch ONNX?

PyTorch ONNX refers to the process of exporting models built in PyTorch to the Open Neural Network Exchange (ONNX) format. This allows models trained in PyTorch to be used in different frameworks and environments that support ONNX, such as TensorFlow, Caffe2, or various deployment tools. The ONNX format provides interoperability and flexibility, making it easier to deploy machine learning models across different platforms. PyTorch provides built-in functions like `torch.onnx.export()` to facilitate this conversion. Using ONNX can help streamline workflows for developers working in production or research settings.

What are common challenges when converting models from PyTorch to ONNX?

When converting PyTorch models to ONNX, professionals often encounter challenges like unsupported operators, dynamic input shapes, or differences in layer implementations between frameworks. These issues may require modifying the original PyTorch model or using custom export functions to ensure compatibility. Collaboration between data scientists and software engineers is crucial to troubleshoot export errors and validate model performance in the target ONNX runtime environment, ensuring the model behaves consistently across platforms.

What skills and qualifications are needed to work with PyTorch ONNX?

To thrive as a PyTorch ONNX Engineer, you need a strong background in deep learning, proficiency in Python programming, and experience with PyTorch and model optimization techniques. Familiarity with ONNX (Open Neural Network Exchange), model conversion workflows, and deployment tools like TensorRT or ONNX Runtime is typically required. Analytical thinking, problem-solving, and clear communication are key soft skills that help in collaborating with cross-functional teams and troubleshooting complex issues. These skills are essential for successfully building, optimizing, and deploying machine learning models across diverse platforms.

What is the difference between Pytorch Onnx vs Machine Learning Engineer?

AspectPytorch OnnxMachine Learning Engineer
Primary RoleModel conversion and interoperabilityDeveloping, deploying, and optimizing ML models
Skills RequiredDeep learning frameworks, model export, PythonML algorithms, programming, data analysis
Work EnvironmentAI/ML teams, software developmentData science teams, product development
Industry UsageModel deployment, cross-platform compatibilityModel development, research, and deployment

While Pytorch Onnx focuses on converting and deploying models across platforms, Machine Learning Engineers design and optimize models for various applications. Both roles require knowledge of ML frameworks and programming, but their core responsibilities differ significantly.

Senior System Software Engineer - Dynamo-Triton Inference Server

Santa Clara, CA • On-site

NVIDIA Gruppe
Computer and Electronic Product Manufacturing • 10K+ employees
Learn more about NVIDIA Gruppeexternal

$184K - $287K/yr

Other

Posted 22 days ago

Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 18 frontline employees who took The Breakroom Quiz

6th of 247 rated software companies

Job description

We are looking for a Senior System Software Engineer to work on Dynamo-Triton Inference Server. NVIDIA is hiring software engineers for its GPU-accelerated deep learning software team. Academic and commercial groups around the world are using GPUs to power a revolution in AI, enabling breakthroughs in problems from image classification to speech recognition to natural language processing. We are a fast‑paced team building a highly‑performant AI inference platform to make design and deployment of new AI models easier and accessible to all users.

What you’ll be doing:
  • Develop world‑class GPU-accelerated AI inference serving software.
  • Contribute to feature development and drive broad customer adoption.
  • Drive the convergence of the Triton Inference Server and NVIDIA Dynamo stacks to establish a unified, high‑performance inference platform. This platform will ensure feature parity and effectively serve both Large Language Model (LLM) and non‑LLM workloads.
  • Be an active member of the open source deep learning software engineering community.
  • Balance a variety of objectives such as building robust software designed to be deployed in production server or cloud environments, optimizing and balancing prediction throughput and latency, and developing and adopting the next generation of inference technologies.
What we need to see:
  • MS or PhD in Computer Science or relevant field (or equivalent experience).
  • 5+ years of professional experience working on deep learning software.
  • Excellent Rust & C++ skills, familiarity with Python, and strong programming & software design skills including debugging, performance analysis, and test design.
  • Experience with high‑scale distributed systems and ML systems.
  • Strong communication skills and ability to work in a fast‑paced, agile team environment.
Ways to stand out from the crowd:
  • Prior experience with AI frameworks and engines, such as TensorRT, PyTorch, ONNX, OpenVINO, vLLM, or TRT‑LLM.
  • Knowledge of GPU memory management, cache management, or high‑performance networking.
  • Experience with distributed systems programming.
  • Experience in contributing to a large open source project: use of GitHub, bug tracking, branching and merging code, OSS licensing issues handling patches, etc.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is $152,000 USD – $241,500 USD for Level3, and $184,000 USD – $287,500 USD for Level4.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until May1,2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

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What Nvidia employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

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About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

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