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Pytorch Developer Jobs in Seattle, WA (NOW HIRING)

VeridianTech

Python Automation Developer (Onsite)

Seattle, WA · On-site

$57.25 - $78.75/hr

Hi, Job Title: - Python Automation Developer Location: - Renton, WA / Everett, WA Full-time ... TensorFlow and PyTorch; 4. Framework: Django ; Flask; FastAPI 5. Web Scraping and HTTP: Beautiful ...

Nvidia

Senior Software Engineer, AI Networking

Seattle, WA · On-site

$139K - $183K/yr

Knowledge in PyTorch, CUDA, and NCCL libraries. * Proven software engineering/development skills ... With competitive salaries and a comprehensive benefits package, NVIDIA is widely regarded as one of ...

Nvidia

Senior Deep Learning Software Engineer

Redmond, WA · Hybrid

$137K - $180K/yr

We are looking for a Senior Deep Learning Software Engineer to design and build our automated ... Contributions to PyTorch, JAX, or other Machine Learning Frameworks. * Knowledge of GPU ...

Sesame

ML Model Serving Engineer

Bellevue, WA · On-site

Required : • Expert in some differentiable array computing framework, preferably PyTorch. • ... Significant systems programming experience; ex. Experience working on high-performance server ...

Mastech Digital

Python Engineer

Redmond, WA · On-site

$75 - $80/hr

We are currently seeking a Python Engineer for our client in the IT Services domain. We value our ... PyTorch (even though the role is not ML-heavy). Team Culture: - Strong emphasis on collaboration ...

General Robotics

Systems Engineer

Redmond, WA · On-site

$155K - $205K/yr

Optimize GPU/CUDA workloads and accelerate ML frameworks (PyTorch, JAX, TensorFlow) for robotics ... Strong systems programming skills in one or more of: C++, Rust, Go, Python. * Solid understanding ...

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Pytorch Developer information

What is a PyTorch Developer?

A PyTorch Developer is a software engineer or data scientist who specializes in using PyTorch, an open-source machine learning library, to build and deploy deep learning models. Their responsibilities typically include designing neural network architectures, training and evaluating models, and optimizing code for performance. PyTorch Developers work in fields such as artificial intelligence, computer vision, and natural language processing, collaborating with teams to solve complex problems using machine learning. They are proficient in Python and have a strong understanding of deep learning concepts. Additionally, they often contribute to research, development, and the deployment of AI solutions in production environments.

What are the key skills and qualifications needed to thrive as a Pytorch Developer, and why are they important?

To thrive as a Pytorch Developer, you need strong programming skills in Python, a solid grasp of machine learning concepts, and experience with deep learning frameworks—especially PyTorch itself. Familiarity with tools like CUDA, Jupyter Notebooks, and version control systems (e.g., Git) is typically expected, along with knowledge of cloud platforms or relevant certifications. Problem-solving ability, effective collaboration, and clear communication are crucial soft skills for success in this role. These skills and qualities are vital for efficiently building, optimizing, and deploying machine learning models in real-world applications.

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

AspectPytorch DeveloperMachine Learning Engineer
Required CredentialsBachelor's or higher in CS, experience with PyTorchBachelor's or higher in CS, data science, or related field, with ML experience
Work EnvironmentResearch labs, AI startups, tech companies focusing on deep learningTech companies, finance, healthcare, often involving deployment and scaling ML models
Industry UsagePrimarily in AI research and development teamsAcross industries implementing ML solutions in production

While both roles require knowledge of machine learning and experience with PyTorch, a Pytorch Developer mainly focuses on developing and optimizing deep learning models using PyTorch. A Machine Learning Engineer often has a broader scope, including deploying, maintaining, and scaling ML models across various platforms and industries.

What are some common challenges Pytorch Developers face when deploying machine learning models to production environments?

Pytorch Developers often encounter challenges when transitioning models from research to production, such as optimizing model performance for inference speed and memory usage, ensuring compatibility with deployment frameworks like TorchScript or ONNX, and managing dependencies across different systems. Additionally, integrating PyTorch models into existing software stacks and maintaining reproducibility can be complex. Collaborating closely with DevOps and data engineering teams is crucial to address these issues and ensure smooth deployment.
What are popular job titles related to Pytorch Developer jobs in Seattle, WA? For Pytorch Developer jobs in Seattle, WA, the most frequently searched job titles are:
What job categories do people searching Pytorch Developer jobs in Seattle, WA look for? The top searched job categories for Pytorch Developer jobs in Seattle, WA are:
What cities near Seattle, WA are hiring for Pytorch Developer jobs? Cities near Seattle, WA with the most Pytorch Developer job openings:
Pytorch Developer jobs near you
Infographic showing various Pytorch Developer job openings in Seattle, WA as of May 2026, with employment types broken down into 86% Full Time, 2% Part Time, and 12% Contract. Highlights an 80% Physical, 5% Hybrid, and 15% Remote job distribution.

PyTorch with Triton performance Engineer

VDart, Inc.

Bellevue, WA

Other

Posted 2 days ago

Job description

PyTorch with Triton performance Engineer

Bellevue, WA (Onsite)

Contract / FTE


Job Summary
      Design and implement high intensity stress workloads using PyTorch and Triton to identify performance bottlenecks and improve platform stability and maturity

Job Description          
       Design and implement high intensity stress workloads using PyTorch and Triton Exercise core MAIA execution paths including compute memory DMA and collectives Enable early detection of performance cliffs stability issues and system bottlenecks across simulator and real hardware Improve platform maturity reduce latestage escapes and increase confidence for broader internal and external adoption Develop PyTorch workloads stressing modellevel execution such as large GEMMs attention patterns MoElike behavior mixed precision and longrunning loops Author custom Triton kernels to stress hardware execution units memory hierarchies and synchronization paths Build parameterized stress harnesses scalable by problem size number of devices and runtime duration Integrate workloads with existing profiling monitoring and failure triage tooling Collaborate with platform firmware and SDK teams to target known risk areas and emerging issues Document usage patterns and provide reproducible scripts for lab and continuous integration CI usage

Roles and Responsibilities :         Develop and maintain a library of reusable PyTorch stress workloads Create Tritonbased micro and macrokernels designed specifically for stress and saturation testing Build and support test harnesses and scripts for singledevice and multidevice execution Ensure workload designs align with platform risk areas and emerging hardwaresoftware issues Collaborate crossfunctionally with platform firmware and SDK teams to refine stress tests Provide comprehensive documentation describing workload intent configuration options and expected stress characteristics Support profiling monitoring and failure triage by integrating stress workloads with existing tools Deliver reproducible and scalable testing solutions for lab and CI environments

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