Qualcomm AI Hub Workbench lets developers compile trained PyTorch or ONNX models into deployable artifacts targeting a variety of runtimes - LiteRT, ONNXRuntime, or Qualcomm AI Engine Direct SDK ...
Qualcomm AI Hub Workbench lets developers compile trained PyTorch or ONNX models into deployable artifacts targeting a variety of runtimes - LiteRT, ONNXRuntime, or Qualcomm AI Engine Direct SDK ...
Front End Compiler
Santa Clara, CA · On-site
This is where the journey begins - you'll build the systems that parse, validate, and lower representations from frameworks like PyTorch, StableHLO, ONNX, and MLIR dialects into our internal compiler ...
Front End Compiler
Santa Clara, CA · On-site
This is where the journey begins - you'll build the systems that parse, validate, and lower representations from frameworks like PyTorch, StableHLO, ONNX, and MLIR dialects into our internal compiler ...
Senior Machine Learning Researcher, On-Device Optimization
Palo Alto, CA · On-site
$150K - $250K/yr
... with PyTorch, ONNX, TensorRT, OpenVINO, QNN, or Llama.cpp Understanding of tradeoffs in asymmetric/symmetric quantization, calibration methods, and inference tuning Preferred Skills Experience ...
Senior Machine Learning Researcher, On-Device Optimization
Palo Alto, CA · On-site
$150K - $250K/yr
... with PyTorch, ONNX, TensorRT, OpenVINO, QNN, or Llama.cpp Understanding of tradeoffs in asymmetric/symmetric quantization, calibration methods, and inference tuning Preferred Skills Experience ...
Lead framework and model enablement strategy across PyTorch, ONNX, ExecuTorch, LiteRT, and future runtimes, including compatibility, validation coverage, performance expectations, and support ...
Lead framework and model enablement strategy across PyTorch, ONNX, ExecuTorch, LiteRT, and future runtimes, including compatibility, validation coverage, performance expectations, and support ...
Engineering Manager, Local AI Agents
Santa Clara, CA · On-site +1
Experience with TensorRT-LLM, Ollama, llama.cpp, vLLM, PyTorch, ONNX Runtime, Windows ML, model optimization, or quantization. * Experience with CUDA, GPU performance, local models on consumer ...
Engineering Manager, Local AI Agents
Santa Clara, CA · On-site +1
Experience with TensorRT-LLM, Ollama, llama.cpp, vLLM, PyTorch, ONNX Runtime, Windows ML, model optimization, or quantization. * Experience with CUDA, GPU performance, local models on consumer ...
Engineering Manager, Local AI Agents
Redmond, WA · On-site +1
Experience with TensorRT-LLM, Ollama, llama.cpp, vLLM, PyTorch, ONNX Runtime, Windows ML, model optimization, or quantization. * Experience with CUDA, GPU performance, local models on consumer ...
Engineering Manager, Local AI Agents
Redmond, WA · On-site +1
Experience with TensorRT-LLM, Ollama, llama.cpp, vLLM, PyTorch, ONNX Runtime, Windows ML, model optimization, or quantization. * Experience with CUDA, GPU performance, local models on consumer ...
Staff Machine Learning Engineer - AI/ML Compiler
Santa Clara, CA · On-site
$160K - $240K/yr
Qualcomm AI Hub Workbench lets developers compile trained PyTorch or ONNX models into deployable artifacts targeting a variety of runtimes -- LiteRT, ONNXRuntime, or Qualcomm AI Engine Direct SDK ...
Staff Machine Learning Engineer - AI/ML Compiler
Santa Clara, CA · On-site
$160K - $240K/yr
Qualcomm AI Hub Workbench lets developers compile trained PyTorch or ONNX models into deployable artifacts targeting a variety of runtimes -- LiteRT, ONNXRuntime, or Qualcomm AI Engine Direct SDK ...
Senior MLOps Engineer - Artificial Intelligence
New York, NY · On-site
$114K - $157K/yr
Working knowledge of common ML frameworks such as PyTorch, ONNX, DeepSpeed etc. * Prior experience with cloud-native technologies like Kubernetes, Argo Workflows, Buildpacks, etc. * Experience with ...
Senior MLOps Engineer - Artificial Intelligence
New York, NY · On-site
$114K - $157K/yr
Working knowledge of common ML frameworks such as PyTorch, ONNX, DeepSpeed etc. * Prior experience with cloud-native technologies like Kubernetes, Argo Workflows, Buildpacks, etc. * Experience with ...
Engineering Manager, Local AI Agents
Santa Clara, CA · On-site +1
Experience with TensorRT-LLM, Ollama, llama.cpp, vLLM, PyTorch, ONNX Runtime, Windows ML, model optimization, or quantization Experience with CUDA, GPU performance, local models on consumer hardware ...
Engineering Manager, Local AI Agents
Santa Clara, CA · On-site +1
Experience with TensorRT-LLM, Ollama, llama.cpp, vLLM, PyTorch, ONNX Runtime, Windows ML, model optimization, or quantization Experience with CUDA, GPU performance, local models on consumer hardware ...
Experience with TensorRT-LLM, Ollama, llama.cpp, vLLM, PyTorch, ONNX Runtime, Windows ML, model optimization, or quantization. * Experience with CUDA, GPU performance, local models on consumer ...
Experience with TensorRT-LLM, Ollama, llama.cpp, vLLM, PyTorch, ONNX Runtime, Windows ML, model optimization, or quantization. * Experience with CUDA, GPU performance, local models on consumer ...
Artificial Intelligence (AI) Engineer, Video Analytics, Onsite in Charlotte, NC
Charlotte, NC · On-site
... ONNX, OpenCV, Python, PyTorch, RESTful APIs, Telemetry Tools, TensorRT, and Ultralytics YOLO. Benefits include medical insurance, Dental, Vision, Savings Plan Options, PTO, etc. Keywords: Charlotte ...
Artificial Intelligence (AI) Engineer, Video Analytics, Onsite in Charlotte, NC
Charlotte, NC · On-site
... ONNX, OpenCV, Python, PyTorch, RESTful APIs, Telemetry Tools, TensorRT, and Ultralytics YOLO. Benefits include medical insurance, Dental, Vision, Savings Plan Options, PTO, etc. Keywords: Charlotte ...
Machine Learning Software Engineer II
$99K - $136K/yr
Optimize PyTorch models for production deployment by converting them to ONNX formats. Apply advanced inference optimization techniques (quantization, pruning, ONNX Runtime) and memory-efficient ...
Machine Learning Software Engineer II
$99K - $136K/yr
Optimize PyTorch models for production deployment by converting them to ONNX formats. Apply advanced inference optimization techniques (quantization, pruning, ONNX Runtime) and memory-efficient ...
Engineering Manager, Local AI Agents
Redmond, WA · On-site +1
Experience with TensorRT-LLM, Ollama, llama.cpp, vLLM, PyTorch, ONNX Runtime, Windows ML, model optimization, or quantization Experience with CUDA, GPU performance, local models on consumer hardware ...
Engineering Manager, Local AI Agents
Redmond, WA · On-site +1
Experience with TensorRT-LLM, Ollama, llama.cpp, vLLM, PyTorch, ONNX Runtime, Windows ML, model optimization, or quantization Experience with CUDA, GPU performance, local models on consumer hardware ...
Technical Program Manager -Local AI Agents
Santa Clara, CA · On-site
$150K - $194K/yr
Familiarity with local inference technologies such as TensorRT-LLM, Ollama, llama.cpp, vLLM, PyTorch, ONNX Runtime, or Windows ML, plus model optimization or quantization. * Experience with Windows ...
Technical Program Manager -Local AI Agents
Santa Clara, CA · On-site
$150K - $194K/yr
Familiarity with local inference technologies such as TensorRT-LLM, Ollama, llama.cpp, vLLM, PyTorch, ONNX Runtime, or Windows ML, plus model optimization or quantization. * Experience with Windows ...
Senior Machine Learning Researcher, On-Device Optimization
Spring, TX · On-site
$150K - $250K/yr
... with PyTorch, ONNX, TensorRT, OpenVINO, QNN, or Llama.cpp Understanding of tradeoffs in asymmetric/symmetric quantization, calibration methods, and inference tuning Preferred Skills Experience ...
Senior Machine Learning Researcher, On-Device Optimization
Spring, TX · On-site
$150K - $250K/yr
... with PyTorch, ONNX, TensorRT, OpenVINO, QNN, or Llama.cpp Understanding of tradeoffs in asymmetric/symmetric quantization, calibration methods, and inference tuning Preferred Skills Experience ...
Senior Customer Engineer, Federal
Washington, DC · On-site +1
$118K - $162K/yr
Familiarity with AI frameworks (e.g., TensorFlow, PyTorch, ONNX) and the broader AI/ML lifecycle is essential, along with a strong grasp of practical AI applications. * Modular Data Center Systems ...
Senior Customer Engineer, Federal
Washington, DC · On-site +1
$118K - $162K/yr
Familiarity with AI frameworks (e.g., TensorFlow, PyTorch, ONNX) and the broader AI/ML lifecycle is essential, along with a strong grasp of practical AI applications. * Modular Data Center Systems ...
Analyze HBF integration points in vLLM, SGLang, TensorRT-LLM, PyTorch, and ONNX Runtime. * Identify HBF-applicable AI model families and inference data objects. * Develop memory-use taxonomy and HBF ...
Analyze HBF integration points in vLLM, SGLang, TensorRT-LLM, PyTorch, and ONNX Runtime. * Identify HBF-applicable AI model families and inference data objects. * Develop memory-use taxonomy and HBF ...
... ONNX, OpenCV, Python, PyTorch, RESTful APIs, Telemetry Tools, TensorRT, and Ultralytics YOLO. Benefits include medical insurance, Dental, Vision, Savings Plan Options, PTO, etc. Keywords: Charlotte ...
... ONNX, OpenCV, Python, PyTorch, RESTful APIs, Telemetry Tools, TensorRT, and Ultralytics YOLO. Benefits include medical insurance, Dental, Vision, Savings Plan Options, PTO, etc. Keywords: Charlotte ...
... ONNX, OpenCV, Python, PyTorch, RESTful APIs, Telemetry Tools, TensorRT, and Ultralytics YOLO. Benefits include medical insurance, Dental, Vision, Savings Plan Options, PTO, etc. Keywords: Charlotte ...
... ONNX, OpenCV, Python, PyTorch, RESTful APIs, Telemetry Tools, TensorRT, and Ultralytics YOLO. Benefits include medical insurance, Dental, Vision, Savings Plan Options, PTO, etc. Keywords: Charlotte ...
Technical Program Manager -Local AI Agents
Redmond, WA · On-site
$144K - $187K/yr
Familiarity with local inference technologies such as TensorRT-LLM, Ollama, llama.cpp, vLLM, PyTorch, ONNX Runtime, or Windows ML, plus model optimization or quantization Experience with Windows ...
Technical Program Manager -Local AI Agents
Redmond, WA · On-site
$144K - $187K/yr
Familiarity with local inference technologies such as TensorRT-LLM, Ollama, llama.cpp, vLLM, PyTorch, ONNX Runtime, or Windows ML, plus model optimization or quantization Experience with Windows ...
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?
| Aspect | Pytorch Onnx | Machine Learning Engineer |
|---|---|---|
| Primary Role | Model conversion and interoperability | Developing, deploying, and optimizing ML models |
| Skills Required | Deep learning frameworks, model export, Python | ML algorithms, programming, data analysis |
| Work Environment | AI/ML teams, software development | Data science teams, product development |
| Industry Usage | Model deployment, cross-platform compatibility | Model 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.
Staff Machine Learning Engineer - AI/ML Compiler
Santa Clara, CA • On-site
Qualcomm •
Technology, Communication and Media • 10K+ employees10,000+ employees
Full-time
Re-posted yesterday
Key responsibilities
Design, develop, and maintain the end-to-end compilation pipeline for models targeting various runtimes and hardware backends.
Build and maintain compilation paths for ONNX and PyTorch models, including graph transformations, validation, and compatibility handling.
Build tooling to analyze, profile, and debug compilation failures, performance issues, and accuracy regressions.
Qualcomm rating
8.8
Based on 11 frontline employees who took The Breakroom Quiz
48th of 247 rated software companies
Job description
Company:
Qualcomm Technologies, Inc.
Job Area:
Engineering Group, Engineering Group > Machine Learning Engineering
General Summary:
About the Role
Qualcomm AI Hub is the platform for on-device AI - enabling developers to easily integrate, optimize, and deploy ML models on Qualcomm devices. Qualcomm AI Hub Workbench lets developers compile trained PyTorch or ONNX models into deployable artifacts targeting a variety of runtimes - LiteRT, ONNXRuntime, or Qualcomm AI Engine Direct SDK (QAIRT) - and profile and validate them on real Qualcomm devices hosted in the cloud.
Join the Qualcomm AI Hub Compiler team and own the infrastructure that powers these model compilations. You will work across the full compilation pipeline - from model ingestion and graph optimization to backend dispatch across CPU, GPU, and NPU - ensuring models compile correctly, execute efficiently, and scale across a growing catalog of on-device use cases spanning vision, audio, speech, and multi-modal models.
What You'll Do
Compiler Pipeline & Infrastructure
Model Catalog, Automation & Collaboration
Minimum Qualifications:
• Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 4+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
OR
Master's degree in Computer Science, Engineering, Information Systems, or related field and 3+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
OR
PhD in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
Preferred Qualifications:
Level of Responsibility
Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail disability-accomodations@qualcomm.com or call Qualcomm's toll-free number found here. Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries).
To all Staffing and Recruiting Agencies: Our Careers Site is only for individuals seeking a job at Qualcomm. Staffing and recruiting agencies and individuals being represented by an agency are not authorized to use this site or to submit profiles, applications or resumes, and any such submissions will be considered unsolicited. Qualcomm does not accept unsolicited resumes or applications from agencies. Please do not forward resumes to our jobs alias, Qualcomm employees or any other company location. Qualcomm is not responsible for any fees related to unsolicited resumes/applications.
EEO Employer: Qualcomm is an equal opportunity employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or any other protected classification.
Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.
Pay range and Other Compensation & Benefits:
$160,500.00 - $240,700.00
The above pay scale reflects the broad, minimum to maximum, pay scale for this job code for the location for which it has been posted. Even more importantly, please note that salary is only one component of total compensation at Qualcomm. We also offer a competitive annual discretionary bonus program and opportunity for annual RSU grants (employees on sales-incentive plans are not eligible for our annual bonus). In addition, our highly competitive benefits package is designed to support your success at work, at home, and at play. Your recruiter will be happy to discuss all that Qualcomm has to offer - and you can review more details about our US benefits at this link.
If you would like more information about this role, please contact Qualcomm Careers.
Qualcomm Technologies, Inc.
Job Area:
Engineering Group, Engineering Group > Machine Learning Engineering
General Summary:
About the Role
Qualcomm AI Hub is the platform for on-device AI - enabling developers to easily integrate, optimize, and deploy ML models on Qualcomm devices. Qualcomm AI Hub Workbench lets developers compile trained PyTorch or ONNX models into deployable artifacts targeting a variety of runtimes - LiteRT, ONNXRuntime, or Qualcomm AI Engine Direct SDK (QAIRT) - and profile and validate them on real Qualcomm devices hosted in the cloud.
Join the Qualcomm AI Hub Compiler team and own the infrastructure that powers these model compilations. You will work across the full compilation pipeline - from model ingestion and graph optimization to backend dispatch across CPU, GPU, and NPU - ensuring models compile correctly, execute efficiently, and scale across a growing catalog of on-device use cases spanning vision, audio, speech, and multi-modal models.
What You'll Do
Compiler Pipeline & Infrastructure
- Design, develop, and maintain the end-to-end compilation pipeline powering Qualcomm AI Hub Workbench, from PyTorch and ONNX model ingestion through graph optimization to deployable artifacts targeting LiteRT, ONNXRuntime, or QAIRT on Snapdragon SoCs
- Build and maintain ONNX-based compilation paths using ONNX IR: graph transformation passes, op validation, and opset compatibility handling
- Build and maintain PyTorch compilation paths consuming torch.export output, including dynamic shapes, custom ops, and ATen IR decomposition
- Contribute to ONNXRuntime QNN execution provider: graph optimizations, graph partitioning, and op validation and lowerings
- Collaborate with QAIRT and QNN teams to ensure correct and efficient model execution across CPU, GPU, and NPU backends
- Build tooling to analyze, profile, and debug compilation failures, accuracy regressions, and performance degradations; develop clear, actionable developer-facing diagnostics
Model Catalog, Automation & Collaboration
- Own compilation and validation of models published on Qualcomm AI Hub, ensuring correct conversion and verified performance across supported runtime targets
- Build and maintain automated compilation pipelines and CI/CD evaluation harnesses to scale model onboarding as the Qualcomm AI Hub model catalog grows
- Partner with internal Business Units to onboard models through Qualcomm AI Hub compilation workflows, translating deployment constraints (target SoC, latency budgets, memory limits) into concrete compilation strategies
- Author technical documentation, tutorials, and example notebooks for the Qualcomm AI Hub developer community
Minimum Qualifications:
• Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 4+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
OR
Master's degree in Computer Science, Engineering, Information Systems, or related field and 3+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
OR
PhD in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
Preferred Qualifications:
- 3+ years of industry experience in ML infrastructure, compiler engineering, or AI framework development
- Proficient in Python and C++
- Solid understanding of ML compiler concepts (graph IRs, operator fusion, shape inference, lowering passes, backend partitioning) and hands-on experience with one or more compiler stacks such as MLIR, ONNX, or TVM
- Experience with PyTorch model export (torch.export, torch.compile, FX, ATen IR) and on-device deployment frameworks such as LiteRT, ExecuTorch, or ONNXRuntime
- Familiarity with SoC-level constraints (memory bandwidth, compute precision, NPU/DSP execution) and hardware-specific runtimes such as QAIRT/QNN is a plus
- Experience building automated CI/CD pipelines for model compilation and validation at scale
- Strong written and verbal communication skills; proficiency with git and software engineering best practices
Level of Responsibility
- Works independently on open-ended compiler and infrastructure challenges
- Provides technical guidance and mentorship to team members
- Decision-making has broad impact - affecting compilation correctness, runtime performance, and the developer experience across Qualcomm AI Hub
- Communicates complex compiler and runtime concepts to varied audiences: SoC engineers, BU partners, and external ML developers
- Has meaningful influence on the Qualcomm AI Hub compiler roadmap, model catalog strategy, and cross-team runtime integration priorities
Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail disability-accomodations@qualcomm.com or call Qualcomm's toll-free number found here. Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries).
To all Staffing and Recruiting Agencies: Our Careers Site is only for individuals seeking a job at Qualcomm. Staffing and recruiting agencies and individuals being represented by an agency are not authorized to use this site or to submit profiles, applications or resumes, and any such submissions will be considered unsolicited. Qualcomm does not accept unsolicited resumes or applications from agencies. Please do not forward resumes to our jobs alias, Qualcomm employees or any other company location. Qualcomm is not responsible for any fees related to unsolicited resumes/applications.
EEO Employer: Qualcomm is an equal opportunity employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or any other protected classification.
Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.
Pay range and Other Compensation & Benefits:
$160,500.00 - $240,700.00
The above pay scale reflects the broad, minimum to maximum, pay scale for this job code for the location for which it has been posted. Even more importantly, please note that salary is only one component of total compensation at Qualcomm. We also offer a competitive annual discretionary bonus program and opportunity for annual RSU grants (employees on sales-incentive plans are not eligible for our annual bonus). In addition, our highly competitive benefits package is designed to support your success at work, at home, and at play. Your recruiter will be happy to discuss all that Qualcomm has to offer - and you can review more details about our US benefits at this link.
If you would like more information about this role, please contact Qualcomm Careers.
About Qualcomm
Sourced by ZipRecruiter
Qualcomm is enabling a world where everyone and everything can be intelligently connected. You interact with products and technologies made possible by Qualcomm every day, including 5G-enabled smartphones that double as pro-level cameras and gaming devices, smarter vehicles and cities, and the technology behind the smart, connected factories that manufactured your latest purchase. Our powerful connectivity solutions keep you connected—even in remote areas. Qualcomm 5G and AI innovations are the power behind the connected intelligent edge. You’ll find our technologies behind and inside the innovations that deliver significant value across multiple industries and to billions of people every day.
Industry
Technology, communication and media
Company size
10,000+ Employees
Headquarters location
San Diego, CA, US
Year founded
1985